Exogenous Data for Load Forecasting: A Review

Ram

on Christen

, Luca Mazzola

1 a

, Alexander Denzler

and Edy Portmann

2 b

Information Technology, HSLU - Lucerne University of Applied Sciences and Arts, Suurstofﬁ 1, 6343 Rotkreuz, Switzerland

Human-IST Institute, University of Fribourg, Bd de P

erolles 90, 1700 Fribourg, Switzerland

Keywords:

Smart Grid, Energy Prediction, STLF, Feature Selection, Exogenous Data Analysis.

Abstract:

Electrical power load forecasting deﬁnes strategies for utilities, power producers and individuals that par-

ticipate in a smart grid. While it is well established in planning processes for production and utilities, the

importance of accurate forecasting increases for individuals. The ongoing deregulation of the electricity mar-

ket enables energy trading by individuals, requiring an accurate estimation of the production and consumption.

Research on forecast for aggregated demand shows that including features for the forecast from sources, called

exogenous, additional to the purely historical consumption data allows to obtain higher accuracy. In fact, their

usage demonstrated to be able to explain the large variability observed in the power demand, taking into ac-

count the individual inﬂuences. Anyway, the inﬂuence of exogenous data is hardly investigated for individual

forecasting, due to the minor prevalence of this analysis to date. This review shows the beneﬁt of exogenous

data usage and the necessity of detailed research on the input features and their inﬂuence on detailed, individ-

ual level, forecasts of power demand. Eventually, this contribution is concluded by the presentation of open

issues and research directions for electric smart communities that the authors would like to address.

1 INTRODUCTION

Electrical power is hardly storable. In fact, the en-

ergy production depends directly on the demand. The

higher the demand, the more power must be produced.

That means, any change in the demand has an imme-

diate impact on the grid stability. Therefore, ensuring

a stable grid requires a regulation of the production.

On a producer level, power plants and grid operators

count on a serious planning in order to guarantee a

permanent power demand coverage. They expect to

know the future demand for maintaining short-term

grid balances and for planning grid extensions based

on long-term power demand.

In contrast, on consumer level, the ongoing dereg-

ulation of the energy market allows direct access to

the electricity market. Consumers with own power

production, so called prosumers, become able to share

their own productions with peers and trade energy on

local markets (Mazzola et al., 2020). This drives the

development of smart grids with the intention, to use

the power as close as possible to the source. Electrical

energy should be used more efﬁciently, close to pro-

duction and with less grid usage, thus incurring in less

https://orcid.org/0000-0002-6747-1021

https://orcid.org/0000-0001-6448-1139

infrastructure charges. However, a change to locally

produced sustainable energy increases the volatility

in the power grid and makes it hard to control with

respect to stability. Regarding this issue, it is again

essential for power plants to have an accurate esti-

mation of the power demand in advance, in order to

react on the volatile and fast changing demand. For

prosumers, on the other side, it is equally worthwhile

to estimate the expected power consumption and self-

production capabilities. In fact, this information al-

lows balancing two conﬂicting objectives: optimal

price control on network level with maximised self-

sufﬁciency for prosumer.

The end-user’s consumption itself is affected by

many external factors. Among others, weather condi-

tions and consumer’s behaviour and activities, deﬁne

the circumstances for the consumption. On the other

side, sustainable power production almost completely

depends on weather conditions, such as wind and sun

irradiation. Many studies evidence this dependency

of the power consumption and production on exter-

nal factors; the dependency on so called exogenous

variables. Therefore, load forecasting often include

data from exogenous variables in addition to histori-

cal load data. This additional data seems to provide

promising information for increasing forecast accu-

racy. Furthermore, the enhancement of everyday ob-

Christen, R., Mazzola, L., Denzler, A. and Portmann, E.

Exogenous Data for Load Forecasting: A Review.

DOI: 10.5220/0010213204890500

In Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020), pages 489-500

ISBN: 978-989-758-475-6

489

jects by electriﬁcation, the consequent growing diffu-

sion of consumer grade Internet of Things (IoT) de-

vices and the stream of information through social

networking services open up new possible sources for

exogenous data.

Load forecasting is an activity with differing chal-

lenges. Parameters such as the forecast time window

or the aggregation level can completely change the

focus and imply different challenges. That also ap-

plies for exogenous data as this information is used to

increase the forecast accuracy. Its value for contribut-

ing on better results strongly depends on the forecast

focus. Therefore, it is of paramount importance to

precisely know the level of support provided by each

variable for load forecasting. Although the inﬂuence

of several well studied exogenous variables on the

power consumption is known, the real increase for

forecast quality and certainty is broadly unknown.

This paper gives a review of previous work on

load forecasting with focus on the use of exogenous

variables for predicting power demand. The review

is based on literature searches run between March,

16th and April, 3rd 2020 on both IEEE Xplore and

Google Scholar libraries. While IEEE Xplore pro-

vides publications in computer science and engineer-

ing, Google Scholar has been considered for covering

a wide area of scientiﬁc publications. The literature

research comprises the terms: Short-Term Load Fore-

cast (STLF), power prediction, load forecasting, ex-

ogenous variables / features / data, feature selection

and social media. It considers more than 50 publi-

cations, with a focus on recent trends. In fact, about

85% appeared in 2010 or later (half of which in the

last 3 years). The analysis shows ﬁndings in: a) the

correlation of exogenous data to the power demand,

b) considered data in load forecasting and c) feature

selection approaches for accuracy improvements. In

addition, this paper gives an overview of the most

common applied forecasting methods and points out

the demand of an in-depth analysis on the use of ex-

ogenous data for increasing the prediction accuracy.

The remainder of the paper is organised as fol-

lows: Section 2 presents the use of exogenous data,

by disclosing the impact and advantage of consider-

ing it in load forecasting. Following, Section 3 re-

veals the broad variation of used exogenous data in

the analysed approaches. A grouping and mapping of

the various variables with respect to the application

incidence directs the focus of the research. Section 4

highlights, for various parameters, their information

value in power utilisation forecasting. Additionally,

Section 5 and Section 6 review common feature se-

lection practices. For covering all characteristics of

these variables in load estimation, this part also in-

clude a discussion of methodologies and issues ex-

isting. Based on this review, Section 7 discloses a

revealed knowledge gap about the true information

value of exogenous data in load forecasting, before

our conclusion (Section 8) terminate this contribution.

2 IMPACT OF EXOGENOUS

DATA IN LOAD FORECASTING

This section answers the question about why exoge-

nous data is used in load forecasting. Predicting the

energy consumption is an old endeavor (Gross and

Galiana, 1987). Whether for long-term decisions in

respect to grid assets or short-term load balance es-

timations in modern smart grid approaches, the pre-

diction always needs to be as accurate as possible

with respect to its energy consumption. Balancing

the production and demand of power or trading en-

ergy on a deregulated market among others, require

a high agility of power producers and storage assets

to guarantee at the same time grid stability. Because

of this requirement, both may proﬁt from an accu-

rate and reliable prediction. In an initial effort, ex-

perts purely estimated the power demand for the next

few hours, days or weeks based on the historical be-

haviour. This simple estimation features a high un-

certainty as it assesses the prospective behaviour on

history only, without considering any context to it.

Certainly, it sufﬁces for rough decisions but not for

issues with a ﬁner granularity or a higher complexity,

such as balancing signiﬁcant and quick load changes.

The power demand as well as the production from

sustainable sources depend on higher-level circum-

stances. Power generation units such as Photo Voltaic

(PV), for instance, produce energy depending on the

intensity of solar irradiation. Similarly, the power

demand depends on the operation of electrical loads

which is driven by external factors. In fact, users de-

ﬁne the consumption proﬁle by turning on an off their

devices, under the needs determined by higher-level

circumstances. Accordingly, researchers try to use in-

formation of this external factors, so called exogenous

data, to improve the load forecasting quality. The cor-

relation between the climate and the power consump-

tion have already been discussed half a century ago

(Heinemann et al., 1966). And the results of many

dependency studies demonstrate a positive correlation

with the proposed forecasting approaches, when us-

ing exogenous data in load forecasting.

Using exogenous data as additional source for in-

put data in load forecasting allows for the extrac-

tion of context related features. This features provide

information from higher-level circumstances that di-

CI4EMS 2020 - Special Session on Computational Intelligence for Energy Management and Storage

490

KWh

E1(t+x)

E1(t)

E3(t-x)

E4(t-x)

LD(t-x)

Historical

values

Instantanous

values

Forecast

values

Exogenous Data

Load Data

Input Data

Feature Extraction Feature Selection

Forecast

E1..4

, f

Feedback input for selection

Load

Forecast

Figure 1: Load forecast feature sources.

rectly inﬂuence the power time series. As depicted

in Figure 1 the information from exogenous data can

originate from recordings, current measurements or

forecasts. The extracted features from the exogenous

data can be used together with the information from

power load data for building a pool of features which

describe the target power time series. Alternatively,

exogenous data can also be used decoupled and there-

fore without load data as shown in (Kandil et al.,

2006) where they evaluated an approach for missing

historic load data. In a typical application, the ex-

tracted features pass a selection procedure that pick

a few features with high relevance which are subse-

quently forwarded to the input of the forecast algo-

rithm. For a reﬁnement, the parametrisation of the

feature extraction and selection methods can also con-

sider the output of the load forecast in a feedback

loop.

Exogenous data can be seen as meta-data of the

power load. They provide information about higher-

level circumstances that affect the consumption as

well as the production. As in (L

opez et al., 2017) and

(Janicki, 2017), the literature shows that considering

this additional information can improve the load fore-

cast quality. However, this also shows the high com-

plexity of the dependency of power load on various

inﬂuencing variables. There is no single but also not

a ﬁxed set of variables that completely describe the

consumption nor the production. The broad variation

of exogenous data and their impact on load forecast-

ing is discussed in the following sections.

3 EXOGENOUS DATA IN LOAD

FORECASTING:

CHARACTERIZATION

In this section, an exploration on which exogenous

data are used in load forecasting is provided. En-

ergy consumption forecast mainly extracts key infor-

mation from recordings of the target variable to pre-

dict the future demand. These recordings are scanned

for describing key values under the assumption that

time series similarly continue as in history. The ex-

tracted characteristics that accurately deﬁne history

time series such as frequencies, wavelet components

or patterns that follow a typical structure, serve as in-

put variables for the forecast as in (Chen et al., 2008;

Zheng et al., 2017; Silva et al., 2017; Jiang et al.,

2017). Some approaches, such as Rana and Koprin-

ska (Rana and Koprinska, 2012b; Rana and Koprin-

ska, 2013), try to fully include this source for bet-

ter forecast results. In this work, they claimed to

achieve better forecast results by means of a shift in-

variant transformation of the frequency components

in the exogenous data. However, despite the high in-

formative value of the history data, there is a possible

valuable improvement in forecasting accuracy of the

power load by extending the input variables with ex-

ogenous data.

The literature review revealed a wide range of ad-

ditionally included exogenous data. More than 50 dif-

ferent variables could be identiﬁed from 48 analysed

publications. By clustering them into typology-based

general categories, we were able to identify the fol-

lowing groups:

Weather data (humidity, precipitation, temperature,

wind-chill, etc.)

Calendar data (date, events, moving holidays, sum-

mer break, etc.)

Day information (before/after holiday, (non-) work-

ing day, weekday, etc.)

Socio-economic (economic trends, gdp, # of employ-

ments, etc.)

Demographic information (birth rate, dwelling

count, population, etc.)

Others (no. of sensors, occupants, devices, etc.)

Anyway, our analysis discovered a high variation

in the usage of these factors. Counting the occurrence

Exogenous Data for Load Forecasting: A Review

491

of all variables in the analysed forecast approaches

reveals large differences in the consideration received

by the higher-level categories. With a signiﬁcant gap,

weather, calendar and day information are clearly the

default choices. 50% and more of the approaches in-

clude data from these groups. In contrast, only about

10% or less use socio-economic data, demographic

information or other data to enrich the input variables.

Table 1 shows the inclusion of exogenous data regard-

ing the different higher-level groups in a descending

order. It emphasises the large gap of favoured addi-

tional data sources to the minor ones.

Table 1: Inclusion of exo. data in load forecast.

Weather data 63%

Calendar data 55%

Day information 53%

Socio-economic data 8%

Demographic information 5%

Other data 5%

A breakdown of the variables in weather data

presents four main clusters: (i) temperature related

data with a usage proportion of approx. 37%. It ap-

pears to be the most relevant weather variable and in-

cludes any temperature data such as the air tempera-

ture, dry- and wet-bulb temperatures or the wind-chill

index. Followed by (ii) humidity and (iii) wind-speed

information that equally share about 20%. The sky

coverage (iv) has still a proportion of 12%. The re-

maining 30% comprises rarely used variables such as

precipitation or air pressure information.

A signiﬁcant parameter in load forecast is the fore-

cast time window. Due to different problem repre-

sentations depending on the time window, the liter-

ature separate the forecast time windows predomi-

nantly in four time section: Very-Short-Term Load

Forecast (VSTLF), STLF, Medium-Term Load Fore-

cast (MTLF) and Long-Term Load Forecast (LTLF).

However, the time span deﬁnition for each of them

is differently designated in literature, as shown in

Table 2. This review integrates the VSTLF into

the group STLF because of the minimal differences

shown in terms of exogenous data usage between

those two categories.

Table 3 to 5 provide an overview of the distribu-

tion of three forecast key values and the inclusion of

exogenous data accordingly. Table 3 divides the fore-

cast time window in STLF, MTLF and LTLF. The

forecast resolution and the aggregation level are com-

pared in table 4 and table 5 respectively. In all ta-

bles, the ﬁrst column represents the proportion of the

variables of all evaluated approaches. The columns

after the double line separation represent the distribu-

tion of included exogenous information for each vari-

able. The review focus on the use of exogenous data

in power demand forecasting. Approaches for other

variable forecasts or without exogenous data are out

of this scope. Hence, a change of the focus may yield

different results in statistics.

In Table 3 it is obvious that researcher pay more

attention to STLF when considering exogenous data

as supporting input data. Mid- and long-term fore-

cast have only a proportion of 10% and 5% respec-

tively. However, decoupled from the forecast time

window, historic load data always seems to be a key

input source for load forecast. For all time window,

they have a prominent proportion of approx. 30% of

the considered input data. In contrast, only long-term

forecasts use socio-economic and demographic infor-

mation seriously as additional input variables. Mid-

and short-term forecast time window rather consider

weather and day information whereas calendar data

seems to provide valuable information for all time

horizons.

According to the mainly provided forecast reso-

lution in literature (see Table 4), it is separated in

hourly, daily or yearly forecasts. The forecast resolu-

tion represents the time span of a single forecast value

comprising either an instant demand or a peak value.

Comparing Tables 3 and 4, the proportion of the fore-

cast resolution shows a similar distribution as of the

forecast time windows. This high correlation implies

that forecasts with short time windows usually pro-

vide higher resolutions. The longer the forecast time

window, the lower the forecast resolution. By consid-

ering the inclusion of exogenous data, it shows that

only the proportion of other exogenous data differs

from the relation to forecast window. This difference

appears due to the evaluation of the study from Chen

et al. that analyses the forecast behaviour by increas-

ing the resolution (Chen and Cook, 2012).

The third key variable of the analysed studies in

load forecasting, the node aggregation level presented

in Table 5, points out a strong focus on utility level.

More than 80% of all studies relate to forecasts on

this level. That means, they consider the forecast of

the power demand of numerous end users up to a sum-

marised node on a utility level (i.e. of a grid branch,

accommodation or production unit). The table also

shows, that forecasts on utility and local levels include

load data with a considerably higher proportion than

individual forecasts. However, socio-economic and

demographic data are only used on utility level. On

the contrary, only the individual level includes other

exogenous data in the inputs parameter list.

As evident from statistics, weather variables are

the best scrutinised and most considered exogenous

CI4EMS 2020 - Special Session on Computational Intelligence for Energy Management and Storage

492

Table 2: Deﬁnition of forecasting time horizons.

(Hong, 2010)

(Ma and Ma, 2017)

(Matija

s et al., 2011)

(Mirowski et al., 2014)

(Raza and Khosravi, 2015)

(Mustapha et al., 2016)

(Zor et al., 2017)

(Hammad et al., 2020)

VSTLF 1d <30’ 1h - - - <1h <1h

STLF 2W 30’ - 6h <30d 1h - 1W 1h - 1W 1h - 1W 1h - 2W 1h - 1W

MTLF 3Y 6h -1d <1Y 1W - 1Y 1M - 1Y 1W - 1Y 2W - 3Y 1W - 1Y

LTLF 30d 1d - 1W >1Y >1Y 1Y - 10Y >1Y >3Y >1Y

Table 3: Forecast Time Window and Exogenous Data [%].

Load

Weather

Calendar

Day

Socio-economic

Demographic

Others

STLF 85 29 25 21 22 1 0 2

MTLF 10 27 27 27 18 0 0 0

LTLF 5 29 0 14 0 29 29 0

Table 4: Forecast Resolution and Exogenous Data [%].

Load

Weather

Calendar

Day

Socio-economic

Demographic

Others

Hourly 80 33 17 22 22 0 0 6

Daily 15 29 26 21 21 1 0 1

Yearly 5 20 10 20 10 20 20 0

data. Heinemann et al. studied the relationship be-

tween weather and power load data already about 55

years ago (Heinemann et al., 1966). The study de-

scribes a method to extract weather sensitive compo-

nents from the total daily peak load. However, it only

concerns the load during summer time. In the follow-

ing years, several studies investigated the dependency

of the power demand on the weather behaviour. And

all agree on the existence of a notable correlation be-

tween this two domains. Furthermore, all emphasise

possible improvements in load forecasts by including

weather data such as in (Rahman and Hazim, 1993;

Hern

andez et al., 2012; Sahay and Tripathi, 2014;

Janicki, 2017).

As a result, numerous approaches tried to increase

the forecast accuracy by extending the input vari-

ables with information from various weather variables

(Rahman and Hazim, 1993; Mirasgedis et al., 2006;

Howe, 2010; Chu et al., 2011). Recently, Silva et al.

even deﬁned weather variables and mainly tempera-

ture, humidity and wind speed as the most signiﬁcant

exogenous inﬂuences in STLF (Silva et al., 2019b).

In contrast to utility or local level, the forecast on

individuals takes the direct environment more into ac-

count. So in (Chen and Cook, 2012), where Chen et

al. propose an STLF approach on individual level that

additionally uses the activity in the building. They

Table 5: Forecast Aggregation Level and Exogenous Data

[%].

Load

Weather

Calendar

Day

Socio-economic

Demographic

Others

Indiv. 13 15 23 23 23 0 0 15

Local 5 33 33 17 17 0 0 0

Utility 82 31 25 19 19 4 2 0

spread several motion sensors and monitored a whole

residential area. The recorded motion data allowed

to derive a pattern that provides extended information

to the consumption, which they used to enrich the in-

put variables for the power forecast. Similarly, Wang

et al. proposed an approach that considers the occu-

pancy information to forecast the energy usage of ed-

ucational buildings (Wang et al., 2018). Furthermore,

Tascikaraoglu and Sanandaji looked for relational pat-

terns among correlations of time series of surround-

ing houses (Tascikaraoglu and Sanandaji, 2016). The

proposed approach showed a considerable improve-

ment for short term forecasts against various bench-

mark models using real and high-quality data.

Other than in STLF, forecasts with long time

windows use exogenous data related to higher-level

consumption inﬂuences. This information does not

comprise changes having a direct inﬂuence on single

end user’s behaviour but represent large-scale events.

In fact, load forecasts ahead for several years are

more affected by long term changes such as eco-

nomic, demographic or climatic movements. Hence,

LTLF approaches as in (Chui et al., 2009; Khatoon

et al., 2014) include information about the popula-

tion, Gross Domestic Product (GDP) or geographi-

cal related changes. Huang et al. showed in their

study (Huang et al., 2016a) that the rapidly increase

of the power demand in a city in northeast China

matches with the population growth and the develop-

ment of the local society. The End-use model, a de-

tailed modelling approach for LTLF, breaks down the

energy consumption to single consumers. This model

is applied to estimate the energy consumption for long

time windows. It is based on extensive information

Exogenous Data for Load Forecasting: A Review

493

about the end user that ranges till to the device level.

The approach is discussed by Ghods and Kalantar in

(Ghods and Kalantar, 2008).

4 VALUE OF EXOGENOUS DATA

FOR DIFFERENT LOAD

FORECAST PARAMETERS

This passage narrates on the correlation between ex-

ogenous data types for different forecast parameters

(e.g. forecast horizon, location, climate). Higher-

level conditions have different inﬂuence on the energy

consumption. Derived from statistics in section 3, it

is obvious that the usable information contained in a

speciﬁc exogenous data type mainly depends on the

forecast parameters. Fast or slowly changing values,

for instance, have more or less impact on the accu-

racy depending on the forecast time window. While

economic or climate aspects have a signiﬁcant inﬂu-

ence in long-term forecasts, they provide barely any

useful information for short-term forecasts. In fact,

they appear to be basically stationary for the fore-

casting time period considered. Therefore, their con-

tribution in increasing the accuracy in STLF is very

small. In the study (Gul et al., 2011) Gul et al. inves-

tigate the relationship between electrical power de-

mand and the slowly evolving economic and demo-

graphic variables. On a country level case study for

Pakistan, they discovered a high correlation between

the selected variables and the power demand. Gen-

erally, it can be shown that long-term forecasts use

averaged data or slow changing variables with low

sampling rates. In contrast, fast changing data with

high sampling rates such as sky coverage are primary

meaningful for short-term forecasts.

In (Wang et al., 2018), a study from Wang et al.

on the energy prediction of two educational build-

ings, they show a variation of the most inﬂuencial

factors from one semester to another. The study dis-

cuss the variable importance in a case study of an

hourly energy prediction. As a consequence of the

observed variation in the inﬂuence, they conclude that

the energy usage of these educational buildings follow

rather a semester than an annual basis pattern.

Equally to the effect on the forecast time win-

dow, exogenous data also differently inﬂuences the

forecast accuracy at various aggregation levels. The

lower the forecast level (e.g: room or apartment), the

higher the observable impact generated by single en-

ergy consuming appliances. Information about the

energy consumption for charging a battery of an e-

vehicle, for instance, highly affect the consumption

behaviour of a single end user. On the other hand, the

aggregated load on a city or country level (i.e. on a

utility level) is not considerable affected by the con-

sumption of a single battery charge. The large num-

ber of end users cancels out signiﬁcant changes in a

single load behaviour, due to the so called averaging

effect. This effect of the aggregation level is analysed

by Mirowski et al. in (Mirowski et al., 2014) and Ger-

wig et al. in (Gerwig, 2015) and seems to have a rel-

evant impact in terms of the prediction accuracy.

The geographic location of the considered energy

consumption is one of the variables that deﬁne the

higher-level conditions. The speciﬁc climate, eco-

nomic or lifestyle among others depend on the lo-

cation and inﬂuence the energy consumption signif-

icantly. The effect of the geographic location, how it

deﬁnes the information value of exogenous variables,

becomes evident by observing the weather variables.

Howe features in his thesis (Howe, 2010) the depen-

dency of the region and the inﬂuence of the temper-

ature on the energy consumption. So, in Philadel-

phia for instance, the temperature swings are limited

thanks to the proximity to the ocean although the city

experiences large temperature ranges than Chicago,

from bitter cold winter to hot summer days. This has a

certain impact on the energy consumption. In fact, the

temperature inﬂuence varies among the regions due to

a different use of cooling and heating devices. This

effect is also discussed by De Felice et al. (De Felice

et al., 2013) and Silva et al. in (Silva et al., 2019b) for

the climate in Italy and South America respectively.

Furthermore, in their study De Felice et al. discov-

ered that from all included weather variables only the

temperature demonstrated an evident inﬂuence on the

daily load variation. On the other hand, they also con-

clude that in some cases the use of weather informa-

tion does not show an evident beneﬁt for daily load

forecasting. A similar statement was made by Kandil

et al. in (Kandil et al., 2006). For Hydro-Quebec they

investigated the effect of various weather variables on

the power load for the province of Quebec, in Canada.

Thereby, they discovered that only temperature has a

serious inﬂuence. Other weather variables like sky

condition (cloud cover) and wind velocity showed no

relation to the load. A brief overview of the inﬂu-

ence of the weather factors on the electrical demand

is given in (Janicki, 2017).

Using weather variables in load forecasting

mainly means the inclusion of some weather vari-

able forecasts in the input variable list. Douglas et

al. investigated in (Douglas et al., 1998) the ef-

fects of the uncertainty of the variable forecasts on

STLF. The performed analysis presents differing im-

pacts of the temperature forecast errors in the vari-

CI4EMS 2020 - Special Session on Computational Intelligence for Energy Management and Storage

494

ous annual seasons. Also L

opez et al. discovered a

non-linear dependency of the load on temperature in

opez Garc

ıa et al., 2013). According to this study,

the non-linearity makes raw temperature data insufﬁ-

cient for using in load forecasting. The data need to be

contextualised for a meaningful use. However, they

also proposed a STLF approach for Balearic Islands

that considers the solar radiation, cloudiness and wind

velocity without temperature and claimed, that the use

of all variables in combination outperforms all other

variants (L

opez et al., 2017).

As exemplary shown on weather variables, the

various exogenous data provide information with a

different value. It highly depends on the target of

the forecast - the location, time window, aggrega-

tion level and so on. Therefore, it is not possible to

uniquely order the variables according to a speciﬁc

relevance or prevalence rank. A proper selection of

the including exogenous data and thereof the applying

features is indispensable. The following section give

an overview about the various feature selection meth-

ods and discusses the need for identifying the most

appropriate forecast methodology.

5 FEATURE SELECTION

METHODS

In this part it is concisely explored the role of fea-

ture selection and its relation to exogenous variables

for different settings of load forecasting. Using ex-

ogenous data in load forecasting makes a careful fea-

ture selection fundamental. The data should provide

a considerable added value to the basic information

from historic load data for reaching more accurate

forecasting results. The power demand is a high com-

plex system that depends on many external factors;

and some provide information with a higher value

than others. Typically, it is straight forward to assume,

that more information would lead to a higher forecast

accuracy. However, the forecast accuracy does not

strictly monotonous increase with a growth in feature

number. Too many and redundant features may even

drag down the forecasting performance, either by in-

troducing inessential information or by accumulating

the effects of the noise present in any real measure-

ment. This behaviour is discussed by Cheng et al.

in (Cheng et al., 2017). As a consequence, it is cru-

cial to include only features producing a considerable,

non-negligible increase in the forecast performance.

However, those selected features do not necessarily

show always a high correlation with the target data.

In the case of sudden changes in the contextual situa-

tion, even features without a high correlation lead to

a more robust forecaster, as described by Drezga and

Rahman in their study (Drezga and Rahman, 1998).

In a common forecast pipeline, valuable features

are deﬁned in an extraction and selection process

that precedes the deﬁnition or training of the fore-

cast model. The forecast output, on the other hand,

may have a direct feedback on the preceding fea-

ture deﬁnition process as illustrated in Figure 1. The

state-of-the-art feature selection approaches usually

apply a multi-step selection procedure. Additional

steps break down the feature set to a few crucial vari-

ables. Commonly used distance related metrics for

deﬁning feature’s information value comprise corre-

lation functions, Mutual Information (MI) or Fisher

Information (FI). This distance metrics quantify the

feature’s similarity to the load data and are used as

measure for the inﬂuence on it (Rana and Koprinska,

2012a; Cai et al., 2018; Hu et al., 2015; Huang et al.,

2016a). However, the relation between two variables

can also show a dispersed non-linear characteristics

such as it is often shown for the temperature and load.

For this case, Silva et al. claim more accurate rela-

tion indices when using models that estimate the ex-

istence condition and not only the linear dependency

as the Pearson Linear Correlation method (Silva et al.,

2019a). The approaches for the selection reﬁnement

are very broad and comprise methods of Machine

Learning (ML) (Niu et al., 2010; Rana and Koprinska,

2012a), wrappers (Hu et al., 2015), minimum Redun-

dancy Maximum Relevance (mRMR) (Huang et al.,

2016a), Permutation Importance (PI) (Huang et al.,

2016b) or Random Forest (RF) (Cheng et al., 2017).

Finally, a qualitative feature selection requires

clean data sets, affected by a minimised noise com-

ponent. As all raw data, exogenous data usually con-

tain outliers or spikes that do not correlate with target

data and distort the valuable information. For a rea-

sonable beneﬁt from the additional information, the

anomalies need to be rejected. The advantage of such

a preprocessed ﬁltering is shown in several studies as

in (Guan et al., 2013; Mustapha et al., 2016; Saleh

et al., 2016). The same holds, if only certain parts

of exogenous data provide valuable information. In

such a case, ﬁlters need to cancel out the remaining

parts before the model uses the data for training and

forecast.

6 LOAD FORECASTING

METHODOLOGIES

In this section, on top of the basic approaches reported

in literature, the selection importance of some spe-

ciﬁc features is detailed. A broad review of various

Exogenous Data for Load Forecasting: A Review

495

STLF methodologies is presented in Srivastava (Sri-

vastava et al., 2016). This study, in accordance with

other literature analysed, separates the models mainly

in two groups: statistical and machine learning ap-

proaches. Statistical models usually explicitly de-

scribe a mathematical relationship between multiple

variables. Therefore their applicability is limited in

case of large number of variables as well as for highly

non-linear, complex dependencies. Based on this lim-

itation, machine learning approaches gained more at-

tention in recent years. According to comprehensive

reviews of multiple applied methods and models by

Gerwig et al. (Gerwig, 2015) and Hammad et al.

(Hammad et al., 2020), STLF approaches favourably

use machine learning models. For STLF on individual

level, the results in (Marinescu et al., 2013) indicate

equally good performances provided by the analyzed

Artiﬁcial Intelligence (AI) and Autoregressive (AR)

methods. Similarly, (Gerwig, 2015) state compara-

ble results for Artiﬁcial Neural Network (ANN), AR

and hybrid methods of both for individual households

up to 1000 end-users. In contrast, Linear Regression

(LR) shows comparable results only for individual

users while Support Vector Regression (SVR) works

well for more than 32 households. Additionally, this

study also remarks a possible accuracy improvement

by combining clustering methods with ANN or auto-

regressive methods.

Independently from the method chosen, each load

forecast is based on two main components: a model

and a selection of input variables presenting a consid-

erable inﬂuence on the target dimension. Thereby, the

input elements may include recordings, instant values

or other forecast measurements. That holds for ex-

ogenous data but also for load data, as illustrated in

Figure 1. For weather input variables, models mostly

use forecast variables. These seem to provide a higher

information value than historical data or instant val-

ues. In fact, the use of historic weather data requires

that load forecasts also comprise weather models in

order to map the information from historical data to

forecast load behaviours. The other way round, when

using forecasts of the weather variables, customised

models do the mapping of the information from his-

torical data to future scenarios separately (Zhu et al.,

2018). Yet, in this case, the uncertainty of the weather

variable forecast directly enters into the load forecast

model. This yields to a forecast, that implicitly com-

prises the uncertainty of the input variable.

Recursive forecast approaches show a similar ef-

fect. Models such as recursive Kalman ﬁlters or

Bayesian estimations use the previously calculated

data point of the same time series as base for the cal-

culation of the subsequent point, as reported in (Dou-

glas et al., 1998). Due to the recursive calculation,

the forecast errors of previous data points directly af-

fect the calculation (i.e. the forecast of the next data

point). Because of this effect - the accumulation of

the prediction errors - all recursive models can have a

signiﬁcant drift in the forecast results.

The effect of forecast errors in weather variables

on load forecasting models is discussed in (Douglas

et al., 1998; Fay and Ringwood, 2010). Douglas et al.

note that a sizeable portion of the load forecast error

is due to a lack of accuracy in the weather forecast.

In order to minimise this effect, Taylor and Buizza

present an approach in (Taylor and Buizza, 2003) that

uses an ensemble prediction system. It estimates the

midday power demand from the density function of

an ensemble of 51 weather-related demand scenarios.

Another approach propose Fay and Ringwood with a

model fusion technique (Fay and Ringwood, 2010).

They claim, that fused forecasts are often more accu-

rate than any individual model forecasts. The model

in the applied case study comprises four independent

sub-models that feed a fusion algorithm. In a for-

mer study they found already, that decomposing load

data into parallel series is advantageous due to the

degree of independence of parallel series. Equally,

Tascikaraoglu points out the positive effect of decom-

posed forecasting in (Tascikaraoglu and Sanandaji,

2016) and Boroojeni et al. proposed a similar ap-

proach in (Boroojeni et al., 2017). In the latter, they

separate the forecast in multiple models by means

of an extended Seasonal Auto-Regressive Integrated

Moving Average Model (SARIMA). This allows to

map multiple seasonality cycles to the power demand

forecasting.

The various load forecasting methodologies pro-

cess the input data in different ways. This has, as

shown in literature, a notable impact on the forecast

quality. Therefore, it is meaningful to deﬁne the fore-

cast methodology depending on the input variable list.

7 FURTHER RESEARCH

This part presents some gaps we noted and our fu-

ture research directions in this domain. Knowledge

about the future power demand is valuable for plan-

ning and controlling the power distribution system.

The increasing electriﬁcation of consumers devices

and the deregulation of the energy market enhance the

demand for accurate forecasts. Especially for end-

user and prosumer, it is beneﬁcial for participating

on deregulated energy markets in the near future. In

parallel, the growing data recordings from innumer-

able consumers devices opens access to new exoge-

CI4EMS 2020 - Special Session on Computational Intelligence for Energy Management and Storage

496

nous data. The diffusion of social media platforms,

digital social networking services and the electrical

enhancement of everyday objects can constitute new

sources of exogenous data for energy behaviour pre-

diction. To list only a few, posts on social media plat-

forms, status information from in-house and mobility

smart appliances or wearable device data embed in-

formation relevant for factor estimation of electricity

consumption. Anyway, in literature these new sources

represent a minimal fraction of the exogenous vari-

ables considered for power demand forecasting, as

can be observed from the category Other data in Ta-

ble 1 (see Section 3). For instance, details about a

planned home party from social media channels con-

tain direct information of the end-user behaviour and

may contribute in describing the upcoming power de-

mand, but are currently rarely considered for this pur-

pose. Another peculiarity is their end-user level gran-

ularity. This aggregation scale is better suited for

more precisely describing the energy behaviour of in-

dividuals.

The literature shows a clear potential for increas-

ing the load forecast accuracy by using exogenous

data. Several studies highlight the correlation be-

tween these data series and the power demand and

show the consequent advantage by their usage. Yet for

reasonable accuracy improvements, the interaction of

the consumption behaviour and the exogenous vari-

ables as well as their inﬂuence need to be fully under-

stood.

Despite an intense research on forecast method-

ologies that consider exogenous data, the value pro-

vided by exogenous data is not sufﬁciently explored.

Indeed, studies compare several exogenous variables

with the consumption behaviour and analyse the fore-

cast improvements when considering this additional

information; but they mostly fail to provide an analy-

sis of the real inﬂuence. To date, it seems to be widely

unexplored, which information (i.e. which part of

the exogenous variables) correlate with anomalies in

the consumption or deﬁnes the higher-level contextual

conditions such as the humidity in combination with

the ambient temperature that inﬂuence the perception

of the temperature. This would especially be of in-

terest for demand forecasts on an individual level, as

this level generally presents a high volatile consump-

tion behaviour.

In that issue, we discovered a certain research de-

mand in the analysis of the information value of ex-

ogenous and historical data on the individual fore-

cast level. We see a demand in research on the im-

pact of the describing variables and expect to have

a weighting for the variables; possibly based on a

fuzziﬁcation of the information value. This will play

a twofold role: on one side, structurally considering

and account for the uncertainty, while, on the other

side, allowing the use of a more ﬁne grained rea-

soning, such as for categorical dimensions and their

mapping from/to continuous values, using member-

ship degrees. A detailed knowledge about the inﬂu-

ence of exogenous data on the power load as well as

the information value from historical data on a future

load can be a key value for further research on fore-

cast methods.

For a better use of the inclusion of exogenous data

in load forecasting, the inﬂuence of these informa-

tion needs to be analysed in more detail. Thereby, the

choice of a suitable metric for the information value

builds the base for a proper analysis of their inﬂu-

ence. Secondly, an information value quantiﬁcation

for each candidate exogenous variable is required, by

examining its point-wise correlation with respect to

the power demand time series. Finally, weighting fac-

tors are necessary to control the exogenous variables

effect on the power consumption forecast. To achieve

this, it is crucial to identify the factors averaged quan-

tiﬁed inﬂuence on the power behaviour. In this way,

it should be possible to extract and apply only the key

information from additional variables to power fore-

casts. It is expected that selective inclusion of ex-

ogenous variables in forecasting achieves higher ac-

curacy, by removing redundant and irrelevant noise.

This will be of paramount importance for improve-

ments on energy management at the local community

level, where individual power inﬂux estimation plays

a major role.

8 CONCLUSION

Exogenous data provides additional information us-

able for increasing the load forecast accuracy. This

review shows the signiﬁcantly additional value that

exogenous data can contribute to better predictive per-

formances. Additionally, it demonstrates the need

for a detailed analysis of the extracted features. De-

pending on several factors such as the aggregation

level, area or the forecast time horizon, the variables

show different inﬂuence on the load. Additional vari-

ables from exogenous data show a noteworthy infor-

mation value for increasing forecast accuracy. Espe-

cially weather variables, but also others, show a high

correlation with power demand. This emerges from

several studies ranging from short-term to LTLF and

considering high aggregation levels. In contrast, there

is still a signiﬁcant lack of attention about the use of

exogenous data in load forecast for residential and in-

dividual level. In fact, forecast on individual level is

Exogenous Data for Load Forecasting: A Review

497

generally affected by the high volatility issue. Nev-

ertheless, this last category is increasingly becoming

relevant due to the energy market deregulation and

the possibility for end-users to actively participate in

smart grids. As a consequence a detailed research on

the feature information value from historic or exoge-

nous data show an increased interest.

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