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Half Hourly Electricity Load Forecasting Using Convolutional Neural Network

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In this paper, enhanced Deep Learning (DL) method is implemented to resolve the accurate electricity load forecasting problem. Electricity load is a factor which plays major role in operations of Smart Grid (SM). For solving this problem, we propose a model which is based on preprocessing, selection and classification of historical data. Features are selected by Combine Feature Selection (CFS) using Decision Tree (DT) and Mutual Information (MI) techniques, and then CFS Convolutional Neural Network (CFSCNN) is used for forecasting of load. Our proposed scheme is also compared with other benchmark schemes. Simulation results show better efficiency and accuracy of proposed model for half hourly electricity load forecasting for one day, one week and one month ahead for the data obtained from ISO NE-CA electricity market.
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Half Hourly Electricity Load Forecasting
Using Convolutional Neural Network
Abdul Basit Majeed Khan1, Sajjad Khan2, Sayeda Aimal2, Muddassar Khan1,
Bibi Ruqia3, and Nadeem Javaid2(B
)
1Abasyn University Islamabad, Islamabad 44000, Pakistan
2COMSATS University Islamabad, Islamabad 44000, Pakistan
nadeemjavaidqau@gmail.com
http://www.njavaid.com
3Sardar Bhadur Khan Women University Quetta,
Quetta 87300, Pakistan
Abstract. In this paper, enhanced Deep Learning (DL) method is
implemented to resolve the accurate electricity load forecasting prob-
lem. Electricity load is a factor which plays major role in operations of
Smart Grid (SM). For solving this problem, we propose a model which
is based on preprocessing, selection and classification of historical data.
Features are selected by Combine Feature Selection (CFS) using Deci-
sion Tree (DT) and Mutual Information (MI) techniques, and then CFS
Convolutional Neural Network (CFSCNN) is used for forecasting of load.
Our proposed scheme is also compared with other benchmark schemes.
Simulation results show better efficiency and accuracy of proposed model
for half hourly electricity load forecasting for one day, one week and one
month ahead for the data obtained from ISO NE-CA electricity market.
Keywords: Deep learning ·Classification ·Mutual information ·
Smart grid ·Decision tree ·Convolutional neural network
1 Introduction
Smart Grid (SG) is an advanced form of Traditional Grid (TG). TG is the
connection of different power systems. They are planted away from the p ower
usage areas. Electricity is transferred by long transmission cables. Energy can
only be provided from the main power plant using traditional power structure.
Traditional power system makes very hard to control the energy because when
electricity leaves the power plant, energy firms have no more control on dis-
tribution, and this may cause the loss of energy. SG is used for efficient and
reliable distribution of electricity. It is a two way transmission among utility and
consumer [1]. Utilities and consumers, both are able to monitor the activities
of grid system. With the efficient and smart digital structure energy firms have
better control on power distribution. Power and power consumption is easily
monitored while transferring from source to destination in SG. Through this
c
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L. Barolli et al. (Eds.): IMIS 2019, AISC 994, pp. 172–184, 2020.
https://doi.org/10.1007/978-3-030-22263-5_17
Half Hourly Electricity Load Forecasting Using Convolutional . . . 173
technology, energy which is produced by renewable resources can also be added
in main power grid. SG also reduced the cost of operations for utilities and low
electricity cost for consumers.
Smart meters are used to record the consumer’s load of electricity consump-
tion and send back to utilities. For the efficient and reliable prediction of future
power consumption, accurate electricity load forecasting is necessary. In this
research, we use different Artificial Neural Network (ANN) and Deep Learning
(DL) based classifiers to forecast the better accuracy of load. DL techniques help
us to find hidden patterns from the large data accurately. Data pre-processing is
also performed to calculate valuable features from large dataset. These methods
give us accurate prediction of load which beats previous methods. The goal of
this research is to provide efficient method to forecast the electricity load with
higher accuracy.
1.1 Problem Statement
In SG technology, we have a lot of issues regarding energy utilization and
distribution. Both the customer and utility want to get advantages from the
technology. This can be only possible, when the efficient utilization of energy is
occurred. Electricity Load forecasting has a huge impact on reducing electricity
consumption in SG. One of the key problems in power grid system is accurate
forecasting of electricity load. When electricity load is forecasted accurately, it
helps power generators and distributors to modify their power grid operations,
and generate electricity according to the need of consumer. Moreover, when
the generation is reduced, cost is also reduced. To solve the forecasting issues
of different models, some advanced and efficient methods are required. We use
enhanced DL strategies to overcome the issues of accurate electricity load fore-
casting and improve the accuracy of forecasting load.
1.2 Contributions
In this paper, accurate load prediction is our primary goal. For this purpose, a
DL based technique is proposed. We proposed a model, which reduces the com-
plexities and gives better results. The contributions of this paper are describes
as follows:
Selection of best features using Decesion Tree (DT) and Mutual Information
(MI) is made at the start of process.
Extract the important features by Combine Feature Selection (CFS).
Design a CFSCNN network, which is the combination of CFS and CNN,
where the best input is provided to the network, which improves performance
of model.
Our model also improves the accuracy, reduce computational complexity and
respone time.
174 A. B. M. Khan et al.
2 Related Work
Electricity price and load, are the most important factors in electricity market.
To make the market competitive and beneficial, price and load forecasting are
key approaches in SG, to be implemented. Large datasets are difficult to process
with traditional computational and statistical models [1], however, author pro-
posed an effective DL based framework for better forecasting of electricity price
and load. Firstly, data pre-processing is done, then Hybrid Feature Selection and
Extraction (HFSE) is used for prediction of electricity load and price. However,
this model has over-fitting problem. Another method is presented in [2], which
forecasts short term electricity load. In this paper author shows the calendar
effects of intra-day, weekly and seasonal on load forecasting accuracy. The effect
of dataset length on accuracy of different neural networks and Support Vector
Regression (SVR) techniques are also studied. Also measures how the forecasting
accuracy effects on the granularity, which makes forecast. Authors also conclude
that computational complexity of Neural Netwok (NN) is greater than LR tech-
niques, and if the historical load is not available, calendar effects become more
important.
In [3] authors present short-term load forecasting of holidays using fuzzy
enhanced same day method. The initial step is to determine the important fea-
tures of the holiday’s profiles. For each feature, one similar day is considered.
The important load features of the expected holiday are acquired from these
same days. The holiday’s load is forecasted by combination of these features
and then improved using a fuzzy method. Accuracy of model is needs to be fur-
ther improved. To forecast cooling load, Deep Auto Encoders (DAE) are used in
[4]. It gives accurate results. Efficient energy utilization can reduce the shortage
of energy, minimize the electricity cost [5]. Another method [6] which devel-
ops a technique for Short-Term Load Forecasting (STLF) using Dynamic Mode
Decomposition (DMD) by choosing the meaningful hidden patterns from data.
The proposed model optimize the load series data that is influenced by different
factors consist of day, time, seasons, climate, and socio-economic activities. In
[7], long term load forecasting is done using twelve different US Western utlilites.
Peak demand growth and load consumption is considered. The simulation results
showed that complexity and accuracy of different forecasting methods is cor-
related. In paper [8], Enhanced Logistic regression (ELR), Classification and
Regression Tree (CART), RFE, RF and Grey Wolf Optimization (GWO) tech-
niques are used for forecasting the electricity load and price. This work is working
well in their model.
In [9], a hybrid model for load and price forecasting is presented. The pro-
posed technique uses kalman and wavelet machines. Load and price data is
decomposed into various frequency components and Kalman machine is used to
forecasts each frequency component of load and price data. A novel load forecast-
ing model is developed in [10], which is consist of a feed forward ANN to forecast
demand of hourly load for different season of a year. In this method, a Global
Best Particle Swarm Optimization (GPSO) is implemented, which improves the
accuracy and efficiency of ANN prediction. To modify network training, fitness
Half Hourly Electricity Load Forecasting Using Convolutional . . . 175
fuction is introduced and weight bias method is also presented. The output of
model shows better results than benchmark techniques. In paper [11], authors
examine the short term price and load forecasting using different selection meth-
ods and deep learning techniques. In [12], authors propose seer grid, an alterna-
tive of Smart Grid communication Network (SGN) method, aimed to minimize
the privacy-utility trade-off. As a result of two-level electricity load forecasting
in seer grid, high relationship occurs among predicted and actual energy con-
sumption values at cluster level, which show excellent utility protection. The
main goal of this study is to find substitute practical design for privacy-sensitive
production and sharing of energy utilization data from the SM to the energy
company which allows operation of the energy company in terms of accuracy.
Another approach for distributed system electricity load forecasting is presented
in [13], which gives short term load forecasting with high accuracy using Support
Vector Regression (SVR) and two-step hybrid parameters enhancement method.
Residential electricity load forecasting has been playing an important role in
smart grids. A Recurrent Neural Network (RNN) based forecasting with appli-
ance consumption sequences is proposed to optimize such volatile problem. Kong
et al. [14]. Electricity load forecasting is complicated problem because of com-
plex and variable factors. In [15], authors introduce an Internet of Things (IoT)
model to automatically finds important feature from the obtained data and ulti-
mately provides an accurate estimation of electricity load. A biggest advantage
of this technique is that its two step forecasting method, which clearly increases
the prediction accuracy for daily total utilization. A new effcient hybrid model
for forecasting is proposed in [16], which consists of GELM, IWNNs, wavelet
processing and bootstrapping. Simulation results show the better accuracy and
reliability of model. A method consists of fractal geometry functions to forecast
electricity load density growth in an urban area to better distribute load density
is presented in [17]. Furthermore, the method presents a very low global error
according to the position of loads, when compared with actual data.
The behaviour of energy consumption has been changing over the few
decades, especially due to improvements in the distributed production segment
and technological innovations introduced by SM. Authors in [18]proposeto
build an ANN and fuzzy logic for electricity load forecasting to do an efficient
analysis. This method is able to give calculations of the elasticity of electricity
demand behaviour with satisfactory results. In [19], authors forecast the load
and price based on long short term memory (LSTM). Proposed Deep (DLSTM)
for prediction on ISO NE-CA and NYISO dataset. This work is effective for only
DLSTM model.
3SystemModel
Our methodology is based on the DL techniques. DL techniques help us in the
better forecasting of load in electricity. The method is based on supervised learn-
ing; having input and output variables. Benchmarks are already defined, which
are used later for comparison of predicting and actual results. The structure
176 A. B. M. Khan et al.
of our proposed model is shown in Fig. 1. It consist of three parts, i.e., data
pre-processing, feature selection and classification.
Fig. 1. Proposed system model
3.1 Preprocessing Data
The first step of our model is to preprocess the data. We used half hourly elec-
tricity load data of ISO-NE CA market. One year half hourly load data of 2017 is
used in this model. Data is divided into two parts i.e. training and testing data.
Train test splitt() is used for splitting the data into; feature train, feature test,
Labels test, Labels train, and these values are further usead as input. Testing is
done on 25% of data and remainig part is used for training. For the fix number
of inputs, random state is also defined. Data is also normalized at this stage.
3.2 CFS Feature Selection
In this section, we describe the method of feature selection. DT and MI tech-
niques, calculate important features from data. We also drop the features, which
have low importance. After combining the results of these two techniques we
select the features by defining a thresh hold value, which drops the unimportant
features. DT importances are shown in Fig. 2while MI importances are shown
in Fig. 3. We found the importances in vector form.
Half Hourly Electricity Load Forecasting Using Convolutional . . . 177
Fig. 2. DT importances
Fig. 3. MI importances
178 A. B. M. Khan et al.
3.3 Load Forecasting
In our proposed model, electricity load forecasting is done with CFSCNN, which
is described as.
CFSCNN In machine learning, CNN is a class of deep neural network. It consist
of one or more convolution layers, and then followed one by one fully connected
layer as in NN. CNN contains input layer, multiple hidden layers and output
layer. Hidden layers consist of, convolutional layer, dense layer, droupout layer,
flatten layer and pooling layer. The convolution layer calculates the output of
neurons that are associated with local boundary or receptive fields in the input,
each simulates a dot product with their weights and a receptive field by which
they are connected to the input data. FFNN trains the network and also classify
the data.
In our model, we use the output of CFS as input for network and the net-
work is named as CFSCNN. Due to CFSCNN, accuracy of forecasting load is
increased. In proposed model, two convolutional layers are used. The first layer
consist of, 96 filters and 2 kernels and the second layer have 32 filters and 3 ker-
nels. In addition, one max pooling layer with pool size 2 is used in our network.
Pooling layer sums the output of large data into neuron and passes that input
to the next layer. Dropout layer is added to avoid overfitting.
4 Simulation and Discussion
In this section, we describe simulation results of our proposed technique in detail,
for showing accuracy of electricity load forecasting. Our model results are sum-
marized as follows.
4.1 Data Description and Simulation Setup
In this paper, ISO New England Control Area (ISO NE-CA), market data from
January 2017 to December 2017 is used. We consider the half hourly data of
each day. Our dataset is consist of 15 columns and 8,760 instances. Each day
consist of 48 instances. For this purpose we use a simulator, which consist of
Python framework with Intel Core i3, 4GB RAM, and 500GB hard disk. Before
moving to next step we also normalize the data.
4.2 Simulation Results
After preprocess the data, we apply our techniques to get result. Results are
described in following sections.
Half Hourly Electricity Load Forecasting Using Convolutional . . . 179
CFS Feature Selection Selection of best features helps in accurate prediction
of load. Two techniques, i.e., DT and MI are used to find the importances of
features. Figure 2shows importances of DT and Fig. 3shows the importances of
MI for different features. As we see in graphs that MI shows more appropriate
feature than DT. After finding the importances, features which have low impor-
tances are dropped by denining a thresh hold value. Features are selected by
combining the importances of both DT and MI. The output is then named as
CFS.
Daily, Weekly and Monthly Comparison with Actual Load In our model,
half hourly load data is used for comparisons. In this section, daily, weekly and
monthly forecasting results of CFSCNN are compared with actual values. One
day prediction is consists of 48 values of day, one week prediction consist of
330 values of a week, and one month prediction consists of 1440 values of a
month. Figures 4,5and 6show these comparisons. Solid line shows the actual
load and dashed line shows the predicted load. The proposed model predicts the
load with very small error, which makes the model accurate. This comparison
shows that, weekly and monthly load forecasting results are better than daily
load forecasting result, which shows the better accuracy of our model with larger
input.
Fig. 4. Comparison graph of CFSCNN with actual load for one day
180 A. B. M. Khan et al.
Fig. 5. Comparison graph of CFSCNN with actual load for one week
Fig. 6. Comparison graph of CFSCNN with actual load for one month
Daily, Weekly and Monthly Comparison with Existing Classifiers In
this section, we compare the daily, weekly and monthly results of our proposed
model with existing classifiers. Accuracy of our model is better than existing
techniques, which is shown in Figs. 7,8and 9. Similarly, forecasting for larger
inputs is much better than lesser inputs. The model, reduces the computational
complexity of existing classifiers. Adding more layers in model helps in better
training of model. However, the training of NN model is very difficult, because
some times it takes random values, which overfitt the data and gives bad accu-
racy.
4.3 Performance Metrics
For calculate the performance and accuracy of model, Two evaluators i.e. Mean
Square Error (MSE) and Mean Absolute Error (MAE), are assumed. Error com-
parison of daily, weekly and monthly load forecasting are shown in Figs.10 and
11. These graphs show that CFSCNN has low error than other techniques, which
Shows better accuracy of proposed model.
Half Hourly Electricity Load Forecasting Using Convolutional . . . 181
Fig. 7. Comparison graph of CFSCNN with different classifiers for one day
Fig. 8. Comparison graph of CFSCNN with different classifiers for one week
Fig. 9. Comparison graph of CFSCNN with different classifiers for one month
182 A. B. M. Khan et al.
Fig. 10. Comparison of MSE score
Fig. 11. Comparison of MAE score
5 Conclusion
A half hourly load forecasting model by combining a feature selection model
and DL based model is presented in this study. The combine feature selection
is used as input for the DL model, which avoids the model from unimportant
calculations and improve accuracy. Important features are examined, and from
among them suitable features are selected by CFS. The experimental results of
different forecasting models showed that the proposed model CFSCNN reduced
computational complexity and increased accuracy efficiently.
Half Hourly Electricity Load Forecasting Using Convolutional . . . 183
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