An Enhanced Convolutional Neural Network
model based on weather parameters for
short-term electricity supply and demand
Zeeshan Aslam, Nadeem Javaid, Muhammad Adil, Muhammad Tariq Ijaz, Atta ur
Rahman, and Mohsin Ahmed
Abstract Short-term electricity supply and demand forecasting using weather pa-
rameters including: temperature, wind speed, and solar radiations improve the op-
erational efﬁciency and accuracy of power systems. There are many weather pa-
rameters which have inﬂuential affect on the supply and demand of electricity, but
temperature, solar radiations, and wind speed are the most important parameters.
Our proposed time series model is based on preprocessing, feature extraction, data
preparation, and Enhanced Convolutional Neural Network referred as ECNN mod-
ule for short-term weather parameters forecasting up to 6-hours ahead. The proposed
ECNN time series model is applied on 61 locations of United States, collected from
National Solar Radiation Database (NSRDB). Model trained on 15-years data and
validated on additional two-years out of sample data. Simulation result shows that
our proposed model performs better than traditional benchmark models in terms of
Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Relative Root
Mean Square Error (RMSE%) performance metrics. Result shows that the proposed
model is effective for short-term forecasting of temperature, solar radiations, and
wind speed. Moreover, proposed model improves the accuracy and operational efﬁ-
ciency of power systems.
Electric power industry plays very important role in the well being of a country, it’s
efﬁcient performance helps in the economic development of a country. One of the
main mode of electric power generation is thermal, which is costly and emits car-
bon in large amount . Rapidly growing interests in renewable energy sources such
as wind and solar power and decreasing cost of power generation, renewable en-
Zeeshan Aslam, Nadeem Javaid (Corresponding Author), Muhammad Adil, Muhammad Tariq Ijaz,
Atta ur Rahman, and Mohsin Ahmed
COMSATS University Islamabad, Pakistan, email: firstname.lastname@example.org
2 Authors Suppressed Due to Excessive Length
ergy generation gains lot of importance because of less expensive and no carbon
emission. Globally, wind and solar power generation grows rapidly from 80 to 790
GW from 2006 to 2016. Accurate forecasting is important because 1% reduction in
Mean Absolute Percentage Error (MAPE) means 0.1% to 0.3% reduction in gener-
ation cost of electricity, which is approximately $1 million annually on large scale
The supply and demand of electricity is highly dependent on weather parame-
ters, therefore, market participants need a useful and reliable technique to increase
their proﬁt ratio by accurately forecasting weather parameters. Weather parame-
ters have great affect on both supply and demand side of electricity. From large
number of weather parameters, temperature, wind speed, and solar radiations are
the most inﬂuencing factors in supply and demand of electricity. Temperature has
great effects on the individual side of electricity, whereas, wind speed and solar
radiations effected the on supply side of electricity. Accurate short-term forecast-
ing of these weather parameters is important for several reasons including: efﬁcient
supply management of electricity, to reduce amount of electricity consumption, to
improve energy efﬁciency level of stations, to prepare effective production plans, to
improve operational efﬁciency, and to adjust and control power stations [1-2]. How-
ever, inaccurate forecasting of these weather parameters is one of the most important
challenge in supply and demand of electricity.
Traditionally, short-term weather parameters forecasting techniques are based
on statistical models such as Vector Autoregression (VAR) model, regression tech-
niques, and artiﬁcial intelligence models such as Support Vector Machine (SVM),
Artiﬁcial Neural Networks (ANNs), and Deep learning models. These models re-
quire extensive memory and computation time, slow convergence, less accurate,
and require weights adjustment and heavy preprocessing in order to reﬁne input
which make them less accurate, and lead to overﬁtting problem. Moreover, as the
size of data increases, these models become complex and requires more training
time . Based on above mentioned limitations of traditional models, we propose
ECNN weather model for short-term forecasting of three weather parameters in-
cluding: temperature, wind speed, and solar radiations using 61 locations data of
In the light of above mentioned weather parameters forecasting techniques, this
paper have following contributions. First, we identify and remove outliers from
weather parameters time series. Second, we apply Fast Independent Component
Analysis (FastICA) to reduce dimensionality of features. Third, we apply Grange
Causality (GCA) and Augmented Dickey-Fuller (ADF) tests to check the useful-
ness and stationarity of time series. After that, we ﬁnd and adjust trend and seasonal
patterns in time series. Fourth, we employ ECNN time series model for short-term
forecasting of three weather parameters to overcome the limitations of above men-
tioned traditional models. Finally, we evaluate the performance of our proposed
model with traditional benchmark models according to three accuracy metrics in-
cluding: RMSE, RMSE%, and MAE.
Rest of this paper is organized as follows. In section II, we investigate and sum-
marize the related work of short-term weather parameters forecasting. In section III,
Title Suppressed Due to Excessive Length 3
we present our proposed methodology. In section IV, we examine the forecasting
performance using simulation results. Finally, in section V, we draw conclusion and
future work of this paper.
2 Related Work
Related work based on weather parameters have been extensively studied and cat-
egorized into four sections: solar radiations forecasting, wind speed forecasting,
temperature or load forecasting, and multivariate weather parameters forecasting. In
paper [26, 27, 28, 31, 32], authors propose solutions for short-term to medium-term
electricity price and load forecasting. In [29,30], authors perform wind and photo-
voltaic power forecasting. Table 1 shows the summary of further related work based
on short-term weather parameters forecasting.
Modeling three weather parameters for short-term forecasting ﬁrst requires prepro-
cessing of input features. Afterward, it requires appropriate extraction and prepa-
ration of input features to reduce dimensionality and keep necessary information.
Then, deﬁne a suitable structure of ECNN to perform short-term forecasting of input
weather parameters. Following sub-sections describe each module of our proposed
system model, before describing these steps in detail, an overview of methodology
3.1 Overview of methodological approach
In this paper, we propose an approach for short-term forecasting of multivariate time
series based on historical input features known as: wind speed, solar radiations, and
temperature. In our proposed approach, ﬁrst apply preprocessing on input features,
then identify and remove outliers using Z score technique, and lastly normalize in-
put features using min-max normalization (see Section 3.3). After preprocessing
of input features, an appropriate feature extraction technique FastICA is applied to
reduce the dimensionality of features (see Section 3.4). After preprocessing and ex-
traction of relevant data, identify and adjust its speciﬁc characteristics and seasonal
patterns (see Section 3.5) in order to prepare data for forecasting. After the prepara-
tion of input features, the ECNN model is develop and pass input data to ECNN. In
order to deﬁne the structure of ECNN, it is necessary to deﬁne elements of ECNN
(see Section 3.6). Overview of proposed approach is presented in Fig. 1.
4 Authors Suppressed Due to Excessive Length
Table 1: Summary of short-term weather parameters forecasting
VAR, 2018  Authors employ VAR model for weather parame-
Linear in nature, need more memory, high compu-
tation time and preprocessing.
Authors propose a hybrid model for multivariate
time series forecasting.
Overﬁtting, extensive computation and memory
requirements, and random weights selection.
DNN, 2018  Authors apply deep learning to forecast solar radi-
Overﬁtting, local optima, and slow convergence.
Authors propose a hybrid model to forecast solar
Overﬁtting and computationally expensive.
Authors propose a global DNN model for solar ir-
Overﬁtting, poor accuracy, and TPE become ex-
pensive on large data set.
Authors propose a hybrid model for solar irradi-
Computationally expensive, more training time,
more memory, and Less accurate on large data set.
Authors propose a hybrid model for short-term
Computationally expensive, overﬁtting, more
training time, and less accurate on large data set.
Authors propose a hybrid model for short-term
Require parameter tuning, overﬁtting, extensive
memory and computation requirements, and less
SVR, 2017  Authors propose SVR model for short-term fore-
casting of demand response.
Overﬁtting, slow convergence, requires more
memory, less accurate, and computationally ex-
pensive on large data set.
Authors propose a hybrid model to forecast wind
Computationally expensive, overﬁtting, requires
more memory, complex and expensive on large
Authors propose a hybrid model for short-term
forecasting of wind speed.
Non-linear characteristics, poor accuracy, less ac-
curate and expensive on large data set.
Authors propose a hybrid model to forecast load
and weather parameters.
Overﬁtting, local optima and slow convergence,
and requires more memory.
Authors propose a hybrid model for wind speed
Overﬁtting, extensive memory and computation
requirements, and GSO become expensive on
large data set.
Authors propose a hybrid model to forecast wind
Overﬁtting, extensive memory and computation
requirements, and GWO become expensive on
large data set.
Authors propose a hybrid model for short-term
wind speed forecasting.
Overﬁtting, slow convergence, and extensive
memory and computation requirements.
3.2 Weather Data
We apply our proposed model on 61 locations of United States, collected from Na-
tional Solar Radiation Database (NSRDB), which is prepared by National Renew-
able Energy Laboratory, National Climate Data center, and other partners . We
use three input parameters from data set. Input parameters that have inﬂuential ef-
fect on both the supply and demand side of electricity such as temperature in Kelvin
Title Suppressed Due to Excessive Length 5
Fig. 1: Overview of proposed system model
(K), wind speed in meters per second (m/s), and global solar radiations in Watt hour
per square meter (Wh/m2). We use 15-years data from 1991-2006 to train model and
additional 2-years data for validation.
3.3 Data Preprocessing
In preprocessing module, we perform three operations: data cleansing and analyz-
ing, outliers detection and removal, and data normalization. In data cleansing and
analyzing, we ﬁrst analyze the data by identifying the type of time series and con-
taining them null or incorrect values, then cleaning those values through ﬁlling them
with mean value of time series. In outliers detection and removal, we identify values
using Z score which reside outside the distribution and do not have any major inﬂu-
ence on ﬁnal output. An outlier has serious impact on mean and standard deviation,
and causes to skew the data. In paper , authors deﬁne the formula of Z score to
identify and remove outliers, which is deﬁned as:
Zscore = (Observat ion −Mean)/StandardDeviation.(1)
After ﬁrst two operations in preprocessing module, we normalize the time series
using Min-Max normalization that make it easy for ECNN to handle data in same
scale and increases the training speed of ECNN. It scales the input features between
0 and 1. In paper , authors deﬁne the normalization formula which is deﬁned as:
6 Authors Suppressed Due to Excessive Length
x0= (xmax −xmin)∗(xi−xmin )
(xmax −xmin)+xmin ,(2)
where (xmax −xmin) = 0 when (xmax −xmin ) = 0 for a feature which shows a constant
value for that input feature. Preprocessing module improves the accuracy of our
proposed system, reduces required memory, training time, complexity of model,
and overﬁtting problem.
3.4 Feature Extraction
In feature extraction module, we apply FastICA, which is computationally powerful
method for estimation of independent component analysis. It is 10-100 times faster
than traditional methods for independent component analysis task, which are based
on gradient descent approach. FastICA is used as feature extraction technique to re-
duce dimensionality of features, while retaining key information. It compresses the
features that take less memory to store these features . By reducing dimension-
ality of features, it reduces: the amount of memory required to store features, com-
plexity of model, training time of model, and improves visualization of data because
in high dimensions it is very difﬁcult to understand and visualize data. FastICA is
similar to Principle Component Analysis (PCA) technique that maps collection of
features to uncorrelated features, whereas, FastICA do more by maximizing the sta-
tistical independence (or minimize mutual information) rather than developing just
uncorrelated features. FastICA performs better than PCA and easy to use . It
reduces number of variables in time series.
3.5 Data Preparation
In data preparation module, we ﬁrst apply Granger Causality (GCA) test, which
is statistical hypothesis test to check weather a time series is useful in forecasting
other time series. GCA is used for time series analysis. GCA result shows that if the
probability outcome is less than any αlevel, then the hypothesis would be rejected at
that level . In , authors describe the equation for GCA test which is deﬁned
AjX(t−j) + E1(t),(3)
where pis the lagged observations, the matrix Acontains the coefﬁcients, E1 repre-
sents residual (prediction errors) for each time series. After that we ﬁnd trend and
seasonal patterns in time series and adjust them. Time series data are mainly com-
posed of trend and seasonal patterns. It is possible to decompose the time series
data into major sub-components such as trend and seasonal components to check
their affects on time series data . There are two different decomposition models:
Title Suppressed Due to Excessive Length 7
additive decomposition and multiplicative decomposition. In this paper, we use ad-
ditive decomposition model to get the trend and seasonal components of time series,
which is deﬁned as:
Xt =t rend(T t) + Seasonal (St) + random.(4)
We use additive decomposition model because additive decomposition is useful to
ﬁnd trend and seasonal components when time series change with respect to changes
in weather and do not vary much . Additive model works more efﬁcient in our
time series data than multiplicative model. After ﬁnding trend and seasonal com-
ponents in time series, we make the series stabilize by differencing method. Dif-
ferencing is the most popular method that make the series stable by reducing or
eliminating trend and seasonality , say,
where ytis original time series and yt−1is the lagged version of original time series.
Then apply Augmented Dickey-Fuller (ADF) test to ensure that weather the time
series is stationary or not. The ADF test result shows that time series is stationary
with p-values less than 0.01 for all locations. Results of differencing and ADF sug-
gest that stationarity is not an issue with our time series.
3.6 Model Structure
For the structure of ECNN, certain elements have to be deﬁned as: number of hidden
layers to use in ECNN, activation function, type of optimizer, padding size, window
size, and adjustment of regularization terms to avoid overﬁtting and increase fore-
cast accuracy. As, for the choice of these decisions, there is no optimal choice given
in literature. All of these elements of proposed ECNN found by a trial and error pro-
cess. In ECNN, we use Leaky Relu activation function, which is most commonly
used activation function in deep neural networks. Leaky Relu improves the training
process and reduces vanishing gradient problem of ECNN. For ECNN learning, we
use Adaptive moment estimation (Adam) optimizer, which is extension of stochastic
gradient descent to optimize the model. Moreover, the choice of hidden layers and
activation function depend on the type of data and problem to solve. Fig. 2 shows
the structure of ECNN.
4 ECNN: Our Proposed Model
CNN is a deep neural network, which uses multiple layered neural network structure
to represent the information. CNN was ﬁrst proposed  for automatic classiﬁca-
8 Authors Suppressed Due to Excessive Length
tion of digit images. One of the most important property of CNN is that it continues
to improve as the size of data increases. It successfully improves the performance
with less memory requirements, because CNN is fully connected network and it
has parameter sharing property. We propose an enhanced version of CNN, named
as ECNN, in which we add some additional hidden layers and adjust parameters of
hidden layers to avoid overﬁtting problem and improve model performance. CNN
works in two parts; in ﬁrst part, CNN learns the high level features from the given
input features with weight sharing property, and in second part, CNN ﬂattens the
output of above layers and perform prediction. In ECNN, we add one convolution
layer with ﬁlter size two using Leaky Relu as activation function. Mathematical
equation of convolution layer to perform convolution operation is deﬁned as in ;
where fis the activation function, wis the weight values of kernel, and iis the input
features. In most of deep neural networks, relu is used as activation function. Relu
has key advantages over other activation functions that it does not activate all the
neurons at the same time. However, the limitation of relu activation function is that
it saturates at the negative region, which means that gradient at negative region is
zero. When the gradient is zero, during back propagation all the weights will not be
updated. To overcome such limitation, we use Leaky Relu. In general, it solves the
dying relu problem. After convolution layer we add two dense layers, dense layer
represents a matrix vector multiplication, and the values in the matrix are trainable
parameters that get updated during back propagation. Dense layer is fully connected
layer whose neurons receive input from all the neurons of previous layers. After the
dense layer, dropout layer comes which is used to prevent overﬁtting problem. Dur-
ing training time, at each iteration, number of neurons with some certain probability
is temporarily dropped. The reason is that dropout prevents the network to be de-
pendent on a small number of neurons and force every neuron to be able to operate
independently, which increases the accuracy, shortens the training time and combats
Fig. 2: Proposed Enhanced Convolutional Neural Network
Title Suppressed Due to Excessive Length 9
overﬁtting. The purpose of dropout layer is to not rely on some or combination of
neurons, but to learn different representations to avoid overﬁtting.
In ECNN, we add two Maxpooling layers. Maxpooling layer reduces the amount
of parameters, model computation, dimensionality, and control overﬁtting by reduc-
ing the spatial size of the network. Key advantage of pooling operation is to generate
small feature maps, which summarize the large input feature maps. After that ﬂatten
layer ﬂattens the output of above layers in order to feed next fully connected layers
to perform prediction. Output of a jth hidden layer neuron can be calculated using
where fis the activation function, w(j,l)is the weight between neurons ojand ol,
and pis the total number of neurons . Fig. 2 shows the proposed ECNN model.
ECNN trains using Adam optimizer to update the weights of model iteratively.
Adam has key some key advantages over traditional stochastic gradient descent
optimization algorithms, which are deﬁned as: it is straightforward to implement,
computationally efﬁcient, requires less memory, best for problems that are large in
terms of data or parameters, and appropriate for noisy problems . Following are
the key equations to update the model parameters:
where mand vare moving averages, gis gradient along time t, and betas are hyper-
parameters of the algorithm.
4.1 Performance metrics
In order to evaluate the model performance, there are many performance evaluation
metrics available in literature. In this paper, we consider three standard performance
metrics, which are deﬁned by  as: MAE, RMSE, and RMSE%. Following are
the equations of these metrics; MAE is deﬁned as:
RMSE is deﬁned as:
and RMSE% is deﬁned as:
10 Authors Suppressed Due to Excessive Length
where Nis the total number of input samples, Fiis the actual value, and Oiis the
predicted value. In this paper, we compare our proposed model with existing bench-
mark models including: SVM, VAR, and ANN.
5 Simulation Results
In this section, we discuss the results of our proposed ECNN time series model with
existing benchmark models. We evaluate the performance of our proposed model
with existing benchmark models in terms of MAE, RMSE, and RMSE% perfor-
mance metrics which are describe in Sub-section 4.1. Fig. 3 shows that the proposed
ECNN has very less average (among 61 locations) MAE and RMSE as compared
to other existing models. In terms of accuracy, ECNN performs better than existing
benchmark models. Fig. 4 shows ECNN forecasting results of wind speed, solar ra-
diations, and temperature for California state.
Table II and III summarize the ECNN forecasting results of wind speed, solar radi-
ations, and temperature according to three performance metrics (i.e., MAE, RMSE,
and RMSE%). These tables show the average MAE, RMSE, and RMSE% for three
weather parameters. For weather parameters forecasting, our results clearly indicate
that the proposed ECNN model has very less error rate and more accurate than tra-
ditional models. Furthermore, the execution time of ECNN is less than the VAR
benchmark model. VAR execution time for one location is 6 minutes and 25 sec-
onds, while ECNN has 3 minutes and 50 seconds. Results concluded that our pro-
posed model have better performance than other benchmark models for supply and
demand forecasting of electricity.
In order to solve the problem of short-term weather parameters forecasting, we pro-
pose an ECNN weather model based on three weather parameters including: temper-
ature, wind speed, and solar radiations. The proposed model is based on four major
modules. In ﬁrst module, we perform preprocessing of time series data including:
data analyzing and cleansing, outliers detection and removal, and data normaliza-
tion. In second module, we perform feature extraction using FastICA. FastICA is
used to reduce dimensionality, model complexity, and improve model training. In
third module, we prepare time series data by applying GCA test to check the use-
fulness of time series, ﬁnd and adjust trend and seasonality in time series, and ADF
Title Suppressed Due to Excessive Length 11
Fig. 3: Average 6-hours MAE, RMSE, and RMSE% of ECNN and other models
Table 2: ECNN average MAE, RMSE, and RMSE% of wind and solar forecasting
MAE (m/s) RMSE
0.0084 0.0103 23.15
0.0095 0.0109 26.17
0.0098 0.0115 28.21
0.0105 0.0121 35.62
0.0107 0.0124 38.67
0.0110 0.0126 38.90
0.0114 0.0135 17.15
0.0116 0.0138 20.17
0.0116 0.0140 22.21
0.0118 0.0142 25.12
0.0124 0.0145 27.41
0.0126 0.0146 30.90
test to check stationarity of time series. In fourth module, we perform short-term
forecasting by employing proposed ECNN model. Therefore, the proposed model
gets the advantages of deep neural networks. The simulation results based on two-
years real world time series weather data shows that the proposed model has more
12 Authors Suppressed Due to Excessive Length
Fig. 4: ECNN wind speed, solar radiations, and temperature forecasting results
Table 3: ECNN average MAE, RMSE, and RMSE% of temperature forecasting
MAE (K) RMSE (K) RMSE%
1-hour ahead 0.0988 0.9886 0.15
2-hour ahead 0.0988 0.9889 0.25
3-hour ahead 0.0989 0.9890 0.28
4-hour ahead 0.0989 0.9895 0.32
5-hour ahead 0.0989 0.9898 0.35
6-hour ahead 0.0990 0.9899 0.40
accurate and effective results than existing benchmark models. Altogether it can be
concluded that developing a deep model is complex process, especially when it is
applied to weather parameters. Hence, the deep model development requires exten-
sive effort to determine the optimal number of hidden layers and their parameters
such as hyper-parameters tuning, since there are no clear instructions available for
such process. In future, we enhance the proposed system by incorporating more
weather parameters and improves its performance. Furthermore, we will enhance
Title Suppressed Due to Excessive Length 13
the performance of proposed model through optimization techniques by tuning their
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