Using Environmental Data for IoT Device Energy Harvesting
Prediction
Mansour Alzahrani
a
, Alex S. Weddell
b
and Gary B. Wills
c
School of Electronics and Computer Science, University of Southampton, Southampton, U.K.
Keywords: IoT, Energy Harvesting, Solar Energy Harvesting, Environmental Data, Weather, Machine Learning.
Abstract: There has been significant innovation in the domain of Internet of Things (IoT) as nowadays wireless data
transmission is playing an essential role in various organizations like agriculture, defence, transportation, etc.
Batteries are the most common option to power wireless devices. However, using batteries to power IoT
devices has drawbacks including the cost and disruption of frequent battery replacement, and environmental
concerns about battery disposal. Solar energy harvesting is a promising solution for long-term operation
applications. However, solar energy harvesting varies drastically over location and time. Due to fluctuating
weather conditions and the environmental effects on PV surface condition, output could be reduced and
become insufficient. Environmental conditions including temperature, wind, solar irradiance, humidity, tilt
angle and the dust accumulated over time on the photovoltaic (PV) module surface affects the amount of
energy harvested. To address this issue, a novel solution is required to autonomously predict the harvested
energy and plan the IoT device tasks accordingly, to enhance its performance and lifetime. Using Machine
Learning (ML) algorithms could make it possible to predict how much energy can be harvested using weather
forecast data. This research is ongoing, and aims to apply ML algorithms on historical weather data including
environmental factors to generate solar energy predictions for IoT device energy budget planning.
1 INTRODUCTION
It is estimated by Arm that about 1 trillion devices
will be connected and become part of everyday life
by the year 2035 (Sparks, 2017). The Internet of
Things (IoT) has the potential to make our
surroundings, houses, and vehicles smarter and more
quantifiable (Loh, 2021). Moreover, IoT devices such
as sensors, lights, and meters provide data and
information to smart cities for collection and analysis.
This data can be used to improve services,
infrastructure, utilities, and other aspects of life
(Gyrard, 2018).
A sensory IoT node consists of sensors, a
processor, a transceiver, and a power supply.
Batteries are the most common source of energy used
to supply power to wireless IoT nodes, which can
either be recharged or replaced according to the
installation conditions (Tong, 2011). Batteries are
often the largest part of an IoT device and, in most
cases, will need to be replaced during the system
lifetime—however, the cost of replacing batteries is
a
https://orcid.org/ 0000-0002-0458-773X
b
https://orcid.org/ 0000-0002-6763-5460
c
https://orcid.org/ 0000-0001-5771-4088
frequently higher than the cost of the IoT device
itself. Owing to cost and environmental concerns,
some systems now use alternative energy supplies
which can exploit ambient energy (Khan, 2015).
Energy can be harvested from various renewable and
ambient energy resources, for example,
vibration/movement, wind, solar, heat, or radio
frequency waves (Garg, 2017). Powering IoT devices
through harvesting sustainable energy has proved to
be an effective solution, extending their operation
lifetime and simplifying their installation. Solar
energy has gained more attention due to the
availability of light in many applications, along with
its simplicity and low component cost. However,
solar energy harvesting depends on the site's
conditions, such as geographical location, weather,
solar irradiance, temperature, and solar angle. The
output power of PV panels is dependent on the solar
irradiance. However, PV efficiency is indirectly
related to the other parameters, including relative
humidity, dust accumulation, wind speed, and
temperature (Yang, 2014). Dust accumulation on the
Alzahrani, M., Weddell, A. and Wills, G.
Using Environmental Data for IoT Device Energy Harvesting Prediction.
DOI: 10.5220/0011069700003194
In Proceedings of the 7th International Conference on Internet of Things, Big Data and Security (IoTBDS 2022), pages 197-204
ISBN: 978-989-758-564-7; ISSN: 2184-4976
Copyright
c
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
197
surface causes shading, and hence performance
degradation. A sequence of rain and sandstorms
affects the adhesion of dust on the surface. We need
to be able to understand the complex environmental
conditions which affect the solar energy harvested.
Recently, much work has been done on power
forecasting and analyses of PVs. Different models
have been used for predictive analyses. Statistical and
machine learning (ML) models may give an insight
into the features of data dependencies and illustrate
the importance of individual characteristics
(Bergonzini, 2009).
This research aims to investigate the feasibility of
using ML and environmental data for solar energy
prediction, to increase system performance. It will
focus on predicting the amount of energy available in
the future, based on historical data and future public
weather forecasts. This paper gives a brief background
to IoT devices and energy harvesting, focussing on the
environmental impact on solar energy harvesting and
discuses some prediction algorithms (section 2). Next,
model design (section 3) gives an overview of the
proposed solar energy prediction model components
and workflow, including a description of the data
acquisition needed for the model. Lastly, section 4
provides details of future work.
2 RESEARCH BACKGROUND
2.1 Wireless IoT Devices
Wireless smart sensors are becoming more critical in
the development of the Internet of Things. These
smart devices are aimed to measure and monitor
natural conditions like temperature, dampness, sound,
pressure, air quality (Newell, 2019). A major
complication for such smart IoT devices being
genuinely ubiquitous is their requirement for a long-
term, dependable power supply. Batteries are the most
common option to power wireless devices. Still, they
must be replaced regularly to guarantee continued
functioning. Such a requirement is unattractive since it
implies significant maintenance expenses, particularly
in distant locations. Energy harvesting from solar,
wind, thermal, and RF sources has been suggested to
solve these issues (Khan, 2015).
2.2 Solar Energy for Wireless IoT
Devices
Photovoltaic (PV) cells convert light energy to
electricity. Solar is one of the most promising and
prevalent forms of renewable energy that utilizes
either natural or artificial light to generate electricity
(Piñuela, 2013). Solar energy harvesting enables the
operation of IoT nodes sustainably and can simplify
their deployment. PV energy harvesting, unlike other
harvesting sources such as temperature difference,
vibration or airflow, is widely available in sufficient
quantities to make the powering of low-power IoT
devices practical.
As solar energy harvesting varies significantly
over time, the energy collected must sometimes be
stored to be utilized when the energy source dictates.
As a result, the Harvest-Store-Use method is well
suited to dealing with unstable energy sources
(Choudhary, 2020).
2.3 Environmental Impact on Solar
Energy Harvesting for Wireless IoT
Devices
Solar energy fluctuations are determined by seasonal
climatic and weather variables such as temperature,
hourly solar angle, solar irradiance, the orientation of
the solar panel and tilt angle and shadows (Yadav,
2014). The following sub-sections will explore the
literature on the impact of various environmental
factors on PV module performance.
2.3.1 Effect of Temperature on PV Module
Performance
Around 17-20% of solar energy is converted into
electricity by a PV module. However, some of the
wasted solar energy appears as heat. The increase in
temperature of the PV module has negative impact on
its efficiency. The electrical yield is reduced with an
increase in the temperature of the module (Rahman,
2017). According to D. Du, the efficiency of
crystalline silicon PV cell drops around 0.45%/°C
(Du, 2013). In an area with an irradiation level of
1000 W/m2, the PV temperature increased to around
56 °C, causing the module output power dropped to
from 49.89 W to 29.42 W and the electrical efficiency
decrease 3.13% (Rahman, 2015), furthermore at
around 25 °C ambient temperature is the maximum
PV efficiency can be achieved. The temperature of a
PV module can be calculated by this equation:
Tc=Tamb+(NOCT−20) G/800 (1)
Where,
Tc: Cell temperature
Tamb: ambient temperature
(NOCT): The normal operating cell temperature
G: Irradiance
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2.3.2 Effect of Dust Accumulation and Tilt
Angle on PV Module Performance
The capability of the cover glass on the surface of the
solar cell module to transmit the solar light has a
significant impact on the solar panel's performance.
Over time, the transmittance may decrease due to dust
accumulation on the glass, which depends on the
collection of dust on the panel surface (Sarver, 2013).
After six months of deployment in the Kingdom of
Saudi Arabia (KSA) environment, PV module
harvested power decrease by 50% (Adinoyi, 2013);
furthermore, the power outputs decreased by 20%
after one sandstorm. The dust accumulation on the
surface of the solar PV panel causes the short circuit
current to drop off at a faster rate, mainly when the
density of dust is higher. There is a 1.7 % loss in PV
power per g/m2 when dust accumulates on the surface
of the PV. Both outdoor and indoor circumstances
were validated for this correlation (Dhaundiyal,
2020).
In dusty environments, the tilt angle also has an
impact on dust accumulation. The deposition of dust
particles on the PV surface with tilt angle was studied
by Sayigh et al. (Sayigh, 1985), who experimented in
Kuwait City. Exposing the panels outside for 38 days,
Sayigh and his team observed that 17% to 64% of
plate transmittance is reduced when the tilt angle is
changed from 60° to 0°, respectively. Another study
shows that, after exposing the PV panels outside for
14 days, results reported the efficiency dropped by
37.63%, 14.11%, and 10.95% for the 0°, 25°, and 45°
tilt angle, respectively (Hachicha, 2019).
2.3.3 Effect of Wind Speed and Direction on
PV Module Performance
The dissipation of convective heat transmission of the
solar module is enhanced by air flow, hence lowering
the temperature of the panel and helps to sustain the
conversion performance (Mazón, 2011). In Dhahran,
KSA, for example, the module's temperature is
reduced by around 10 °C by the increase in velocity
of wind from 2.8 to 5.3 m/s (Said, 2015). Several
experiments were held, artificially varying the wind
direction and velocity to examine the performance of
solar PV. Dust particles are blown away from the
surface of solar PV by the wind, diminishing dust
deposition. In Egypt, it is seen that after two weeks of
wind, the rate of dust deposition on a PV panel
surface decreased significantly (Hegazy, 2001).
However, the impact of wind direction is not
appropriately addressed. Wind coming from the
desert should be warmer than from the sea or
lakeside. Hence the direction wind also plays a
significant role in the temperature of the PV module.
2.3.4 Effect of Humidity on PV Module
Performance
It is observed that the solar PV efficiency is increased
when humidity is relatively low. The impact on the
performance of solar modules due to relative
humidity has been verified. The performance yield of
solar cells is enhanced from 9.7% to 12.04% at 60%
to 48% humidity, respectively (Katkar, 2011). As far
as power is concerned, it is noted that with the
increase in relative humidity by 20% in result
approximately 12.4% of power is decreased
(Rahman, 2015). Concerning dust adhesion and
humidity, it is noticed that dust particles stick on the
panel surface due to humidity. In order to restore the
module to its initial power efficiency, cleaning the PV
module's surface is required. In respect of quantitative
analysis, it is seen that adhesion is increased by
approximately 80% when relative humidity is
increased from 40% to 80% (Said, 2014).
2.3.5 Effect of Rainfall on PV Module
Performance
Rainfall has an impact on solar PV, as rain removes
dense dust from the panels. However, some particles
of dust stick on the panel surface due to cementation
and might not be detached. Michel and Muller
(Micheli, 2017) have shown the correlation between
surface cleaning and a rain event. However, a
minimum of precipitated water is required for
effective cleaning. Different reports provide the
minimum rain threshold required for cleaning the
panel, such as minimal of 5mm daily rainfall (García,
2011) and 6.9mm (Toth, 2020). The intricacy of the
surface cleaning can be attributable to the multiplicity
of threshold conditions because they are further
subject to different factors such as dust type,
wettability, speed of droplet, dust adhesion, and
surface inclination state (Ilse, 2018). In dry and semi-
dry regions, where the soiling rate is high and there is
less rainfall, rainfall is not considered sufficient for
dust removal. In such areas, a proper cleaning
mechanism is required for better performance.
2.3.6 Effect of Air Quality on PV Module
Performance
Air quality is an important parameter, since the
accumulation of ambient Particulate Matter (PM) on
the surface is the main reason for PV soiling.
Furthermore, soiling on the PV surface has an impact
Using Environmental Data for IoT Device Energy Harvesting Prediction
199
on the Direct Normal Irradiance (DNI); hence the PV
model performance will be decreased. PM with an
aerodynamic diameter of <10μm (PM
10) or <2.5 μm
(PM2.5 or PMfine) is the measurement of the air
quality. Micheli et al. found that ambient PM10
concentrations yielded better correlation in long dry
period than PM
2.5 concentration (Micheli, 2019). In
studies (Micheli, 2019) and (Coello, 2019), it has
been shown that PM concentrations variability are
important factors in modelling PV soiling. Moreover,
air quality has an impact on the horizontal visibility,
hence the Global Horizontal Irradiance (GHI)
reaching the PV surface is reduced by as much as 40%
to 50%, with a much more substantial reduction in the
Direct Normal Irradiance (DNI) at noon when the
Aerosol Optical Depth (AOD) is 3.0 (Kosmopoulos,
2017). Therefore, air quality is one of the parameters
that will be explored and used in developing a
predictive PV soiling model based on time-series.
2.4 Prediction Algorithms for Solar
Energy Harvesting
In the last couple of years, several prediction
techniques have been used to achieve the solar energy
prediction. These techniques can be distinguished
between Past Predicts the Future (PPF) and Weather
Forecast-Based Techniques (Sharma, 2010). PPF
techniques consider previously available data on how
much energy was harvested and apply that to the
future. These techniques divide the day into a number
of equal-sized time slots, and the prediction is done
either for the next slot based on the previous one or
for the same time slot of the next day. These are
simple techniques and easy to use but fail to give
valuable predictions when the weather changes.
Exponentially Weighted Moving Average
(EWMA) is an algorithm that was proposed by Kansall
et al; the algorithm has shown good predictions
accuracy results (Kansal, 2007). However, the EWMA
prediction accuracy is high only when weather
conditions are consistent. The reason for this is because
of the way the EWMA works. The EWMA algorithm
divides the day into a number of fixed time slots.
Moreover, it predicts the energy harvesting rate based
on the weighted average for that period in previous
days. When the weather changes frequently, and there
is a mix of sunny and cloudy periods, the EWMA
algorithm gives low accuracy predictions.
Piorno et al. proposed the Weather-Conditions
Moving Average (WCMA) algorithm to solve these
shortcomings of the EWMA (Piorno, 2009). It
includes a GAP factor, which is supposed to take into
account the average harvesting of previous days and
compare it to actual harvesting, thus determining the
weather direction change and considering it in the
prediction. While this does make better predictions
compared to EWMA, once again, when weather
changes are more frequent, and the fluctuations are
significantly larger, so are the predicting errors. These
predictions techniques are based on historical data,
i.e. how much energy was harvested in the past.
Nevertheless, this historical approach is unreliable
when weather changes occur between days and even
during a single day. To be able to make better energy
harvesting predictions and to overcome the
limitations of the PPF techniques, such as their short-
term focus, weather forecasts need to be included.
Weather forecasting has been used with historical
data to provide better predictions of future energy
harvesting. A study by (Sharma, 2010) revealed that
weather forecast-based predictions give better results
compared to PPF.
The Artificial Neural Network (ANN) and Support
Vector Machine (SVM) is widely used forecasting
techniques for forecasting the nonlinear time series
data. SVM used for the prediction of daily and monthly
global solar radiation in (Belaid, 2016) proved to
requires few simple parameters to get good accuracy.
The energy predictions can be enhanced through
techniques that allow several profiles to be combined.
The machine learning approach will be applied on
automatic learning and improve the prediction.
3 SOLAR ENERGY PREDICTION
MODEL BASED ON WEATHER
DATA FOR SOLAR ENERGY
HARVESTING WIRELESS IOT
NODES
A solar energy prediction model will allow IoT nodes
to schedule their duty cycles and tasks based on
predicted energy. The goal of the proposed model
(data-driven approach) is to explore the machine
learning algorithms and the associated feature
extraction for better solar energy predictions.
Knowing the solar energy budget ahead of time and
planning the sensor node tasks accordingly, will aid
the sustainable operation of the sensor node.
3.1 Overview of the Solar Energy
Prediction Model
The proposed model has multiple data inputs and
prediction models (See Figure 1).
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Data Acquisition is the key to accurate power
predication. This subsection discusses and describes
the required data for machine learning which will
serve the proposed model in this research.
Historical Weather Data
The data collected is of two different types. The first
data is from King Abdullah City for Atomic and
Renewable Energy (K.A. CARE) for Jeddah City,
hourly data from June 2017 - June 2020. This data
consists of various variables such as air temperature,
global horizontal irradiance, wind direction,
moisture, and zenith angle, etc (See Table 1). The
second data set is from openweathermap.org for the
same period as the first data set. This data consists of
various variables such as visibility, clouds, dewpoint,
rain, time of sunrise, and sunset (See Table 2). The
two data sets will be combined and used to train and
test the prediction model. Dust Accumulation, Air
Quality, and Horizontal visibility are some of the
focuses of this research. Hence, they will be
investigated further, and the results added to the
model to enhance the prediction.
Figure 1: The Proposed Solar Energy Prediction Model.
Weather Forecast Data
The weather forecast data will be gathered from
openweathermap.org via their accessible API
(Openweathermap.org, 2022). This forecast data is
hourly. As part of the data preparation, the coldness
levels need to be changed to numerical values.
Photovoltaic Data
This data includes PV size, efficiency, peak power
point current and voltage, tilt angle, and orientation.
Energy Data from Sensor Node
- Battery Level: The battery level (state-of-charge,
SoC) can be estimated based on the battery voltage
measurement. The battery level will be presented as
a percentage from (0 - 100 %) based on the battery
voltage measurement and the energy storage
properties. This data will be gathered for each time
slot from the sensor node.
- Solar Energy Harvested: The amount of energy
harvested will be calculated based on the
measurement of the current from the solar panel,
considering the charging state. Knowing the charging
state is essential for the machine learning algorithms.
Sun Position and Tilt Angle
The sun position will be calculated hourly based on
the location of the sensor, time of the day, azimuth,
and zenith. The PV tilt angle and the orientation are
important for calculating the incidence angle based on
the sun's position. The amount of solar irradiance
reaching the surface of the PV can be determined by
the incidence angle and the size of the PV.
Prediction Models
In this work, widely used machine learning models
will be used for predictions, including k-nearest
neighbour (k-NN), support vector machines (SVM),
artificial neural networks (ANN). Different tests will
be conducted for fine tuning of each algorithm.
The accuracy of the models is dependent upon
different factors, which are as the following:
The accurate measurement of weather forecast
comparing to the actual weather data.
The missing data is either due to transmission
problems or the failure of nodes at any point in
time.
The following will explain each prediction in the
proposed model:
The Dust Accumulation Prediction: will
estimate the dust accumulation on the PV surface
over time based on the weather forecast data such as
horizontal visibility, air quality, wind speed, wind
directions, and rainfall considering the installation
date. This prediction will have direct influence on the
solar energy harvesting.
The Solar Energy Harvesting Prediction: This
prediction will be based on the historical weather
data, weather forecast data, PV data, the sun position
and tilt angle, and the energy from the sensor node
with respect of dust accumulation prediction.
Using Environmental Data for IoT Device Energy Harvesting Prediction
201
Table 1: list of the data Parameters and Description from (K.A. CARE).
Parameters Description
Air Temperature The degree of hotness or coldness of the environment (measured by C°)
Wind Direction at 3m Average wind direction at 3 meters height (measured by degree from North)
Wind Direction at 3m (std dev) Standard deviation of the average wind direction data at 3 meters height
Wind speed at 3m Average wind speed at 3 meters height (measured by m/s)
Wind speed at 3m (std dev) Standard deviation of the average wind speed data at 3 meters height
Azimuth Angle Defines the direction of the sun, Azimuth Angle is the angle between a line due south
and the shadow cast by a vertical rod on Earth (measured by degree)
Diffuse Horizontal Irradiance
(DHI)
Diffuse Horizontal Irradiance is the amount of radiation received per unit area by a
surface that does not arrive on a direct path from the sun, but has been scattered by
molecules and particles in the atmosphere (measured by W/m2)
Direct Normal Irradiance (DNI) Direct Normal Irradiance is the amount of solar radiation received per unit area by a
surface that is always held perpendicular to the rays that come in a straight line from
the direction of the sun (measured by W/m2)
Global Horizontal Irradiance
(GHI)
Global Horizontal Irradiance is the total amount of shortwave radiation received
from above by a surface horizontal to the ground. This value includes both Direct
Normal Irradiance and Diffuse Horizontal Irradiance (measured by W/m2)
Horizontal Visibility The greatest distance toward the horizon that prominent objects can be identified
visually with the naked eye (measured by km)
Peak Wind Direction at 3m Greatest value of wind direction at 3 meters height (measured by degree from North)
Peak Wind Speed at 3m Greatest value of wind speed at 3 meters height (measured by m/s)
Relative Humidity Relative humidity is the ratio of the partial pressure of water vapor to the equilibrium
vapor pressure of water at the same temperature (measured by %)
Barometric Pressure Atmospheric pressure or barometric pressure, is the pressure exerted by the weight
of air in the atmosphere of Earth (measured by mBar)
Zenith Angle The solar zenith angle is the angle between the zenith and the center of the sun
(measured by degree)
Table 2: Lists of The Data Required for Modelling.
DATA LIST
Historical Weather Data
Air Temperature, Diffuse Horizontal Irradiance (DHI), Direct Normal
Irradiance (DNI), Global Horizontal Irradiance (GHI), Azimuth Angle, Zenith
Angle, Horizontal Visibility, Peak Wind Direction at 3m, Peak Wind Speed at
3m, Wind Direction at 3m, Wind Direction at 3m (std dev), Wind speed at 3m,
Wind speed at 3m (std dev), Relative Humidity, Barometric Pressure, Time and
Date.
Weather Forecast and Alerts Wind Speed and Direction, Temperature, Pressure, Humidity, Air Quality,
Visibility, Clouds, Dewpoint, Rain, Time and Date.
Photovoltaic Data PV Size, Efficiency, Voltage at Peak Power, Current at Peak Power.
Sun Position and PV Tilt Angle Zenith, Azimuth.
Location, time of sunrise and sunset. PV Tilt Angle and Orientation.
Energy Data from Node Battery level, Solar energy harvested.
Battery Gain Prediction: Based on the
prediction of The Solar Energy Harvesting and the
battery level measurement at night when there is no
solar energy, taking into account the energy storage
discharging cycle. The battery level data and the
actual energy harvested data will indicate the energy
consumption for the sensor tasks operation. Hence,
this information will be used for more machine
learning to improve the predictions.
Battery Level Prediction: Based on the
prediction of the battery gain and the energy
consumption for the given tasks. The energy
consumption will be determined by how many tasks
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will be scheduled for that day. The model will use the
data from the previous days to improve the prediction
accuracy over time.
Tasks Scheduling: When scheduling tasks for
IoT node, two factors need to be considered: the
amount of energy will be consumed in that time
period and the maximum utilization from the assigned
energy. Different Tasks Schedules will be tested to
Figure 2: Block diagram for the proposed Wireless IoT
Node Architecture.
4 CONCLUSIONS AND FUTURE
WORK
Wireless IoT sensors are widely used nowadays.
Sensors can be employed in remote locations and
harsh environments. Their operation lifetime depends
on their energy supply. Powering such devices via
solar energy harvesting systems enables the
continuous work. For
optimal use of the available
energy, the IoT device can schedule its operation
according to the available energy. In order to achieve
this, the prediction of the future energy which can be
harvested is required. Therefore, understanding the
effects of the environment on the solar energy
harvesting needed.
This research will be defining the most relevant
weather parameters and scenarios based on the
literature. Identify the possible scenarios help in
assessing the model in several operating conditions.
The developed scenarios will be simulated to
understand the effect of these scenarios on energy
harvesting. For example, if it rains after a dusty day,
what is the impact on solar energy harvesting.
Furthermore, the effective input features for the
prediction will be studied and identified and will be
tested in the simulation phase. The widely used
machine learning algorithms Support Vector
Machines (SVM) is most known as state-of-the-arts
forecasting models based on machine learning. These
models are data-driven, and they are suitable for
short-term as intra-hours and long-term as next-day
forecasts. These forecasting models will be tested in
terms of prediction accuracy and energy efficiency.
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