A NEW NEURAL SYSTEM FOR LOAD FORECAST IN

ELECTRICAL POWER SYSTEMS

A Topological Level Integration of Two Horizon Model Forecasting

Rodrigo Marques de Figueiredo, José Vicente Canto dos Santos and Adelmo Luis Cechin

PIPCA - UNISINOS, Av. Unisinos 950, São Leopoldo, Rio Grande do Sul, Brazil

Keywords: Artificial Neural Networks, Electric Power Systems, Load Forecasting.

Abstract: This work presents a new integrated neural model approach for two horizons of load forecasting. First of all

is presented a justification about the design of a computational neural forecasting model, explaining the

importance of the load forecast for the electrical power systems. Here is presented the design of the two

neural models, one for short and other for long term forecasting. Also is showed how these models are

integrated in the topological level. A neural model that could integrate two forecasting horizons is very

useful for electrical system enterprises. The computational system, here presented, was tested in three

different scenarios, where each scenario has specific electrical load behaviour. At last the results is

commented and explained.

1 INTRODUCTION

Actually the load forecasting is an important tool for

energy enterprises. The forecast for electrical power

systems is subject to internal variables in addition of

external variables, stochastics variables, like

meteorological and macroeconomic variables. The

first one has an imply in residential loads and the

second one has a strong imply in industrial loads

(Ardil et al, 2007). The modern way to develop a

forecaster is by the using of ANN, Artificial Neural

Network, models.

In the literature, there are many papers about the

use of neural modeling for only one forecasting

horizon, examples are the work of Botha (Botha,

1998), Drezga (Drezga, 1999), Saad (Saad, 1999),

Charytoniuk (Charytoniuk, 2000), Fukuyama (Fuku-

yama, 2002), Funabashi (Funabashi, 2002) and

Abdel-Aal (Abdel-Aal, 2004). But neural modeling

for two or more forecasting horizons is scare, one of

the few exmples is the work of Shirvany (Shirvany,

2007).

The present paper propouses a new neural model

for load forecasting by the using of two integrated

models, one for short term and other for long term

load forecasting. The resulting model has the ability

for short and long term load forecasting at the same

time, with better performance, both in response

quality and computational performance.

The electrical power system focused in this

forecast system is located in a large area in the south

of Brazil. All the tests and results showed in this

paper are referred to this system. This area is divided

in seven nodes and each node has one type of the

three electrical consuption behaviour, residential,

industrial or a mixed type. After this introduction,

follows the description of the proposed system, the

tests performed and the results obtained and, finaly,

our conclusions.

2 THE COMPUTATIONAL

FORECASTING SYSTEM

The forecasting system consists in two neural

models, one for short term and other for long term

forecasting. These neural models are given by the

artificial neural network application. The models

were individually designed and validated to later be

integrated. The data base of variables available to be

used to design the models are given by meteorolo-

gical, macroeconomic and electrical variables.

The variable space for an electrical system is too

large, even when it is reduced for the three types

showed above. For a better model response this

363

Marques de Figueiredo R., Vicente Canto dos Santos J. and Luis Cechin A. (2009).

A NEW NEURAL SYSTEM FOR LOAD FORECAST IN ELECTRICAL POWER SYSTEMS - A Topological Level Integration of Two Horizon Model

Forecasting.

In Proceedings of the 6th International Conference on Informatics in Control, Automation and Robotics - Intelligent Control Systems and Optimization,

pages 363-366

DOI: 10.5220/0002199703630366

Copyright

c

SciTePress

space must be reduced. Variable selection methods

are the best way to reduce the variable space

removing from model most of redundant and

irrelevant variables.

2.1 Variable Selection

The variables were selected by the using of forward

selection. In this method the neural model is

constructed by its interaction, where in each interact-

tion one variable is included in model. The criteria

used to the model construction are the minor

response error for a validation (Seeger, 2003). This

algorithm runs until a stop criteria, in this paper case

an error level minor than fifteen percent. For the two

models, short and long term, this method is applied

by individually manner. In the variable selection in

addition to the inclusion of new variables were also

varied the number of neurons in the hidden layer of

ANNs, seeking for the best system performance.

2.2 Long Term Model

The main objective of this model is to provide the

behaviour information of the electrical system to

short term model, through the topological integra-

tion. In this model the forecasting horizon chosen

was the monthly horizon, because that information is

very important for the business of the electrical

energy sector utilities (Quintanilha et al, 2005).

After the forward selection application the

variables were selected, resulting in the neural

model for long term forecasting. The monthly

information of temperature and residential,

commercial and industrial electrical load as input,

with six neurons in hidden layer and one as output,

indicating the long term total load forecast. This

model uses as input the monthly information of one

year and one day ago. That information give to the

long term model the monthly tendency of each

month of the year with all seasonal influences. This

fact makes the model more robust.

2.3 Short Term Model

This model try, as main objective, mimetizes the

electrical power system load behaviour. As like long

term model, this model uses the forward selection to

choose its variables. In this model case faster varia-

bles behaviour is relevant to it, like meteorological

and electrical variables.

After the use of forward selection the neural

model was constructed with the variables selected.

This uses the daily information, about one day ago,

of temperature, humidity and total electrical load as

input, with six neurons in the hidden layer and one

as output, representing the total load for the shot

term forecast.

2.4 Model Integration

The integration of the short and long term forecast

models is the main step of the computational system

design. Is important keep in mind that this integra-

tion is given in the topological level. With this type

of integration only the tendencies of each model are

passed to the other. In other types of integration the

error also is integrated.

The neurons sharing guarantee the tendencies ex-

change between long and short term models without

polluting yours responses. But this is not a total

share, only a parcel of these neurons is shared.

Using the neural models for short and long term

forecasting with six neuron in hidden layer, a new

neural model are created with merging these

models. There were made tests to verify the number

of shared neurons in hidden layer is needed to

bettering the model response. In this test the number

of shared neurons was varied in one to all (twelve).

Figure 1: Trial with neurons sharing.

Figure 2: Neural model integration.

ICINCO 2009 - 6th International Conference on Informatics in Control, Automation and Robotics

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The Figure 1 shows that four is the best number

of shared neurons to this application. The Figure 2

shows the final arrange of neural model, in

highlighting the shared neurons in dark color. Also

are showed the inputs and the outputs of final model.

The final model uses twelve neurons in hidden

layer, with four exclusively used by short term

model, four for the long term model and four

neurons being shared by the two models, unifying

these models in only one.

2.5 System Architecture

The architecture of the computational system is gi-

ven by three main parts, or modules. This architect-

ture is showed in Figure 3.

Figure 3: Computational system architecture.

Database contains all information about the elec-

trical power system. For forecasting models is very

important a large database as possible (Swinder et al,

2007). In the data treatment module the data is

synchronized, normalized and separated per type.

This learning occurs throughout the artificial neural

network (ANN) training. The data set is delivered to

the neural model aligned like is showed in Figure 4.

Figure 4: Data set temporal aligning.

In Figure 4 the forecast instant represents the

moment when the computational forecasting system

is executed. This data alignment avoids the need for

not available data. That case occur when two

forecast-ting horizons are used in the same model

and one horizon is overridden by the other.

3 TESTS AND RESULTS

The system proposed was subjected to three

different scenarios of load consumption being that,

Industrial Load Region, Residential Load Region

and Mixed Load Region. The tests outcomes of the

integrated system are compared with the outcomes

of the separated models for short and long term

forecasting. In the tests was used the same number

of sample for each region data set, and the same data

set to individually forecaster (short and long term)

and the integrated proposed system. There are

performed the Ten-Fold Cross Validation method to

prove the benefit of the models integration. As

quantitative metric was used was the Root Mean

Squared Error (RMSE), and all the results presented

in this section were obtained with this metric.

3.1 Industrial Load Region Test

Industrial load has a seasonal behaviour with strong

dependence of macroeconomic factors, that indicates

the production behaviour of the industry and per

consequence it is your electrical power consumption.

The proposed system and individually models,

developed to create the proposed system, results for

this scenario are showed in Table 1.

Table 1: Industrial region test results.

Forecast

Horizon

Propose

Integrated System

Individually

Models

Long Term 4,6% 21,4%

Short Term 13,2% 23,7%

3.2 Residential Load Region Test

The residential load presents a different behaviour, it

is not seasonal. This type of consumer has a

behaviour closely liked to the meteorological condi-

tions. In cold days the residential consumer uses

their heaters, and in the hot days they use their air

conditioners. The system outcome to this type of

load consumption is given in Table 2.

Table 2: Industrial region test results.

Forecast

Horizon

Propose

Integrated System

Individually

Models

Long Term 6,7% 22,9%

Short Term 13,0% 24,8%

3.3 Mixed Load Region Test

Mixed load consumer regions are areas where there

A NEW NEURAL SYSTEM FOR LOAD FORECAST IN ELECTRICAL POWER SYSTEMS - A Topological Level

Integration of Two Horizon Model Forecasting

365

is a balance between residential and industrial

consumers. In those areas there is no definition

about the load behaviour, because it follows the

trend given by the industrial and residential load.

The system outcome to the mixed type of load

consumption is given in Table 3.

Table 3: Industrial region test results.

Forecast

Horizon

Propose

Integrated System

Individually

Models

Long Term 5,5% 22,1%

Short Term 11,7% 24,6%

In Figure 5 is ploted the results for short term

forecast, comparing the pattern wait with outcomes

of conventional forecasting system and the new

neural system proposed in this paper. Note that the

proposed system (represented by solid black line)

fits perfectly with the pattern waited (grey line), the

conventional neural system, represented by the short

term model (dashed line) before developed has a

worst behaviour.

Figure 5: Short term load forecasting for mixed region.

4 CONCLUSIONS

The results show that integration of long and short

term model is beneficial to the response of the

integrated system. This integration improve the

system accuracy for both forecast horizon and also

turns the resulting model generic. That affirmation

can be proved by the close results for the tree types

of load consumption. A generic forecasting system

has a important advantage for commercial usage,

because they could forecast many instances with

only one model.

Finally, the main contribution of this work is a

new neural model for load forecasting, by the

topological level integration usage. With this

integration, the computational system has proved

flexible and capable to generating excellent results.

Some other aspects of the load forecast in electric

systems, like the expansion of the time horizon, will

be published in future works.

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