A Deep Learning Approach Towards
Price Forecasting Using Enhanced
Convolutional Neural Network
in Smart Grid
Fahad Ahmed1, Maheen Zahid1, Nadeem Javaid1(B
Abdul Basit Majeed Khan2, Zahoor Ali Khan3, and Zain Murtaza1
1COMSATS University, Islamabad 44000, Pakistan
2Abasyn University Islamabad Campus, Islamabad 44000, Pakistan
3Computer Information Science, Higher Colleges of Technology, Fujairah 4114, UAE
Abstract. In this paper, we attempt to predict short term price fore-
casting in Smart Grid (SG) deep learning and data mining techniques.
We proposed a model for price forecasting, which consists of three steps:
feature engineering, tuning classiﬁer and classiﬁcation. A hybrid feature
selector is propose by fusing XG-Boost (XGB) and Decision Tree (DT).
To perform feature selection, threshold is deﬁned to control selection.
In addition, Recursive Feature Elimination (RFE) is used for to remove
redundancy of data. In order, to tune the parameters of classiﬁer dynam-
ically according to dataset we adopt Grid Search (GS). Enhanced Convo-
lutional Neural Network (ECNN) and Support Vector Regression (SVR)
are used for classiﬁcation. Lastly, to investigate the capability of proposed
model, we compare proposed model with diﬀerent benchmark scheme.
The following performance metrics: MSE, RMSE, MAE, and MAPE are
used to evaluate the performance of models.
Nowadays, electricity plays an important role in economic and social develop-
ment. Everything is dependent on electricity. Without electricity, our lives are
imagined to be stuck. Electricity usage areas are divided into three categories:
industrial, commercial and residential. According to , residential area con-
sumes almost 65% of electricity from the whole generation. In the traditional
grid, most of the electricity is wasted during generation, transmission and distri-
bution. To solve this issue, SGs are introduced. A traditional grid is converted
into SG when information, communication and technology (ICT) are integrated
into the traditional grid. SG is an intelligent grid system that manages gener-
ation, consumption and distribution of energy more eﬃciently than traditional
grid . SG provides the facility of bidirectional communication between util-
ity and consumer. As we know, energy is the most valuable asset of this world.
Springer Nature Switzerland AG 2019
L. Barolli et al. (Eds.): EIDWT 2019, LNDECT 29, pp. 271–283, 2019.
272 F. Ahmed et al.
It is very necessary to utilize energy in an eﬃcient way to increase productivity
and to decrease losses and hazards. Energy crises are present everywhere, so
industries are moving toward SG. The primary goal of SG is to keep balance
between supply side (utility) and demand side (consumer) . SG fulﬁlls, all
the demands from the consumer side and gives response to their requests. Con-
sumers send their demands to the utility through Smart Meter (SM). Hence, a
huge amount of data is collected via SM regarding the electricity consumption of
consumers. Electricity usage may vary depending upon diﬀerent factors such as:
wind, temperature, humidity, seasons, holidays, working days, appliances usage
and number of occupants. Utility must be aware of the usage pattern of con-
sumer. , Data Analytics (DA) is a process of examining data. DA is basically
used in business intelligence, for decision making. When data analyst wants to
do an analysis of electricity load consumption and pricing trends, then they take
dataset of any speciﬁc electricity company. To maintain the load of electricity
consumption, many researchers are working on forecasting of electricity load
and price . There are three types of forecasting: Short Term Load Forecasting
(STLF), Medium Term Load Forecasting (MTLF) and Long Term Load Fore-
casting (LTLF). STLF consists of time horizon from a few minutes to hours.
Day ahead is considered in STLF. MTLF contains the horizon from one month
to one year. LTLF consists of time horizon from one year to several years. Dif-
ferent researchers, used diﬀerent types of time horizon for forecasting. STLF is
mostly used for forecasting, because it gives better accurate prediction results
as compared to others. Consumers can also take part in SG operations to reduce
the cost of electricity by energy preservation and shifts their consumption load
from on-peak hours to oﬀ-peak hours. Consumers can utilize energy according to
their requirements. To manage supply and demand, both residential customers
and industries require electricity price forecasting to cope with upcoming chal-
lenges . Robustness, reliability, computational resources, complexity, cost of
resources are some issues however, accurate price prediction is also an important
issue . When the utilization of electricity is maximum then prices are also high
. The price of electricity depends on various factors, such as renewable energy,
fuel price and weather conditions etc. [9,10].
In , they performed price forecasting of electricity through Hybrid Struc-
tured Deep Neural Network (HSDNN). This model is a combination of CNN
and LSTM. In this model, batch normalization is used to increase the eﬃciency
of training data. Authors in , proposed a model of Gated Recurrent Unit
(GRU) in which LSTM is used as a base model for price forecasting accurately.
In paper , authors predict load consumption using Back Propagation Neu-
ral Networks (BPNNs) model. Authors, used this model to reduce forecasting
errors. In , authors proposed a combined model of Cuckoo Search, Singular
Spectrum Analysis and Support Vector Machine (CS-SSA-SVM) to increase the
accuracy of load forecasting. In , authors used data mining techniques such as
k-mean and KNN algorithm for electricity price forecasting. They used k-mean
A Deep Learning Approach Towards Price Forecasting Using ECNN in SG 273
algorithm to make three clusters for weekdays and also used KNN algorithm to
divide the classiﬁed data into two patterns for the months of February to March
and April to January. After classiﬁcation, a price forecasting model is developed.
The price data of 2014 is used as input and results are veriﬁed by 2015 data.
1.2 Problem Statement
We reviewed the related works in electricity price forecasting using deep learning
techniques and feature engineering.
In  this paper proposes an electricity price forecasting system based on
the combination of two deep neural networks, the Convolutional Neural Network
(CNN) and the Long Short Term Memory (LSTM). However, they neglect the
problem of over-ﬁtting. Authors Ziming et al. , worked on price forecasting by
using the hybrid model of nonlinear regression and SVM. However, the big data
is not taken in consideration. Renewable resources, DR, and other factors are
inﬂuenced on price and load [15,17]. The price of electricity changes frequently,
that is why traditional methodologies and approaches are not suitable. We need
some enhanced methods for price predictions.
In this paper, main goal is to predict electricity price accurately by using data
mining and deep learning techniques. To achieve this, we proposed model for
price forecasting. In this work, SVR and CNN both classiﬁers are used for the
prediction of price. Enhanced Convolutional Neural Network (ECNN) is used as
a proposed classiﬁer, its results are compared with diﬀerent benchmark schemes.
However, it is very hard to tune the parameters of these models according to
dataset. The contributions of this paper are summarized as follows:
– Hybrid Feature Selector: Hybrid feature selector is proposed in this paper.
– Overﬁtting: Risk of overﬁtting is mitigated in this model.
– Grid Search and Cross Validation are used to tune the parameters of classi-
ﬁers, by deﬁning the subset of parameters,
– Enhance classiﬁers is used to increase the forecasting accuracy.
2 Related Work
Authors in , discussed price forecasting with the proposed model of Hybrid
Structured Deep Neural Network (HSDNN) in which the combination of CNN
and LSTM is used. The accuracy of this model is compared by performance
evaluators i.e., MAE and RMSE with diﬀerent models. In , authors described
the prediction accuracy with the proposed model of LSTM and RNN named as
Gated Recurrent Units (GRU) compared its accuracy with benchmark models:
SARIMA, Markov chain and Naive Bayes. Rohit et al.
274 F. Ahmed et al.
In , authors discussed the data pre-processing steps. They have worked on
how to choose a technique for feature selection and feature extraction. These
two phases are very important in data pre-processing. Feature selection and
extraction techniques play very important role in forecasting. Pre-processing of
data is a ﬁrst step in every forecasting process. Normalized data provides better
results for accuracy in forecasting. Data, which is present in raw form gives poor
result in prediction. In this work, a meta learning approach is implemented and
recommends the pre-processing technique, which shows better results.
In , authors proposed a model for price forecasting using Deep Learn-
ing approaches i.e. DNN as an extension of traditional MLP, hybrid LSTM-
DNN structure, hybrid GRU-DNN structure and CNN model. Wang et al. ,
authors proposed a hybrid framework of feature selection, feature extraction and
dimensionality reduction by GCA, KPCA and also predict the price of electric-
ity through SVM. In , authors used Stacked Denoising Autoencoder (SDA)
and DNN models. They also compared diﬀerent models including SVM, clas-
sical Neural Network and multivariate regression. Lago et al. , worked on
DNN to improve the predictive accuracy of a market, for feature selection. They
used Bayesian optimization and functional analysis of variance. Also proposed,
another model to perform price prediction of two markets simultaneously. Raviv
et al. , used multivariate models for prediction hourly price instead of uni-
variate, also mitigate the risk of overﬁtting by using dimensionality reduction
techniques and forecast combination. Javaid et al. , proposed a deep-learning
based model for the prediction of price, using DNN and LSTM. They worked
on the prediction of both price and load. In , authors considered a proba-
bilistic model for hourly price prediction. Generalize Extreme Learning Machine
(GELM) is used for prediction. They used bootstrapping techniques, to increased
the speed of model by reducing computational time. Abedinia et al. , focused
on feature selection to performed better predictions. These proposed models,
based on information theoretic criteria i.e. Mutual Information (MI) and Infor-
mation Gain (IG) for feature select. Another contribution of this paper is a
hybrid ﬁlter-wrapper approach.
In [25,26], proposed a hybrid algorithm for price and load forecasting.
Also worked on new conditional feature selection, Least Square Support Vector
Machine (LSSVM) and proposed a new modiﬁcation for Artiﬁcial Bee Colony
Optimization and Quasi-Oppositional Artiﬁcial Bee Colony (QOABC) algo-
rithm. Keles et al. , proposed a method based on ANN. They also used
diﬀerent clustering algorithms to ﬁnd optimal parameters for ANN. Wang et al.
, proposed Dynamic Choice Artiﬁcial Neural Network (DCANN), this model
is used for day-ahead price forecasting. This model is a combination of super-
vised and unsupervised learning, which deactivates the bad samples and search
optimal inputs for a model to learn. In , developed a hybrid model based on
Neural Network. Authors, in , used Multilayer Neural Network (MLNN) for
electricity price forecasting.
A Deep Learning Approach Towards Price Forecasting Using ECNN in SG 275
3 Proposed Model
In this paper, a novel price prediction model is proposed. Figure 1shows the pro-
posed model for price prediction. Proposed model is divided into four modules.
The modules of proposed models are:
1. Feature Selection,
2. Feature Extraction,
3. Grid Search and Cross Validation,
4. Price Prediction using SVR and CNN.
The individual module is further explained in the following subsections.
Fig. 1. Proposed model for price prediction
3.1 Model Overview
The accuracy of prediction is key issue in electricity price forecasting. As dis-
cussed earlier, the electricity price depends on various factors, which make train-
ing of classiﬁers diﬃcult. To improve accuracy of price prediction, hybrid feature
selector (i.e., DTC and XG-boost) is used to select most relevant features. At
ﬁrst, RFE is used to remove dimensionality and redundancy of data. In order to
tune parameters of classiﬁer, GS is used along with cross validation to select best
subset of parameters. Finally, selected features and best parameters are used in
classiﬁers to predict electricity price.
276 F. Ahmed et al.
3.2 Feature Extraction Using RFE
RFE is used to select speciﬁed number of features from dataset. It removes
weakest feature recursively, until the speciﬁed number of features is reached.
RFE requires number of feature to select, however, it is diﬃcult to decide in
advance that how many features are most relevant. To address this issue, cross
validation is used with RFE. Cross validation calculates accuracy of diﬀerent
subsets and select the subset with highest accuracy.
3.3 Feature Selection Using XG-Boost and DT
Using XG-boost and DT, importance of all features is calculated with respect to
target, i.e., electricity price. These techniques calculate the importance of fea-
tures in vector form. The components of this vector, represents the importance of
every feature in sequence. However, we can drop features which have less impor-
tance. The fusion of Xg-boost and DT gives more accurate results. Figure 4
shows the importance of features. To control feature selection, threshold is
used. Features having importance greater than or equal to threshold are con-
sidered and rest of the features are dropped. Feature selection is performed using
Eqs. 1and 2.
Fs =Reserve if IXG[i]+IDT [i]≥(1)
Drop if IXG[i]+IDT [i]< (2)
Where, IXG[i] represents the feature importance calculate by XG-boost,
IDT [i] is the feature importance calculated by DT. is the threshold values
for the feature selection and irepresent feature.
3.4 Tuning Hyper-parameters and Cross Validation
Tuning classiﬁer is very important to do accurate and eﬃcient forecasting. There
is a strong relationship between hyper-parameter and results of classiﬁer. GS is
used to the tune parameters of classiﬁer for higher accuracy. For this purpose,
we deﬁne subset of hyper-parameters for SVM shown in Table 1.
Table 1. Subset of parameter for Grid Search
Parameter name Parameter value(s)
kernel [‘linear’, ‘rbf’]
gamma [‘scale’, ‘auto’, 5,10,20,30,50]
epsilon [0.2, 0.02, 0.002, 0.0002]
A Deep Learning Approach Towards Price Forecasting Using ECNN in SG 277
3.5 Electricity Price Forecasting
After feature selection and parameter tuning, the processed data and the best
subset of parameters are used in SVR and CNN to forecast electricity price.
Hourly price data of two months (November and December 2016) are used to
train classiﬁer and predicts the price for ﬁrst week of January 2017. We compared
the results of ﬁrst January 2017 and ﬁrst week of January 2017 with actual price
of electricity of NYISO. The results of SVR are shown in Fig.5(a) and (b)
whereas, results of CNN are shown in Fig. 5(c) and (d), respectively.
4 Simulation and Results
In this section, the simulation results are discussed in details.
4.1 Simulation Environment
For simulation purpose, we implement the proposed models by using the follow-
ing python libraries i.e. Keras, Tensorﬂow, Sklearn, numpy and pandas. Models
are implemented on a system with Intel core i3, 8GB RAM and 500 GB storage
capacity. Two diﬀerent datasets are selected for simulation. Lastly, Dataset 
is used as input in price prediction model, which is taken from New York Inde-
pendent System Operator (NYISO). However, dataset 2 contains hourly data of
price and electricity generation from 2016 to 2017.
Fig. 2. Result of cross validation (RFE)
278 F. Ahmed et al.
(a) 2nd January, 2017 (b) First Week of January, 2017
Fig. 3. Price prediction without parameter tuning.
4.2 Results of Price Prediction Model
The proposed model is shown in Fig. 1. NYISO dataset  is taken as input,
which contains 9,314 real-world records. However, for the sake of demonstration,
75 days dataset are used to train model. This dataset invariably contain approx-
imately 2000 h record, i.e., from 1st November, 2016 to 15th January, 2017. The
whole simulation process is organized as:
1. Feature extraction using RFE
2. Feature selection by combining the attributes importance calculated by XG-
boost and DT
3. Parameter tuning using cross validation and Grid Search
4. Prediction using SVR and CNN
5. Results and Comparison with real data of January 2017.
Feature Extraction: To remove redundancy and dimensionality of data, RFE
is used. Although, it is diﬃcult to determine in advance how many features set
is required. To resolve this issue, cross validation is used with REF to select
optimal number of features. Cross validation tests every combination of features
and calculates the accuracy of each subset. The subset of features with the
highest accuracy is used for prediction. Figure 2shows the maximum accuracy
score on seven number of features.
Feature Selection: Importance of selected features are calculated by both DT
and XG-boost. By adding both importance, combined importance is calculated.
For selection of features, a threshold value is deﬁned. Features are selected
with importance greater than or equal to threshold value. Figure 4shows the
importance of every feature. Some features have very high importance, i.e., TWI
Zonal LBMP, RTC Zonal LBMP and Load. TWI Zonal Price Version shows
very less importance as compared to others features. Most of the features have
importance greater than 0.15 and that is why we set the values of threshold to
0.15. Those features whose values are less than threshold value are dropped.
A Deep Learning Approach Towards Price Forecasting Using ECNN in SG 279
Fig. 4. Feature importance for price
(a) 2nd January, 2017 (b) First Week of January, 2017
(c) 2nd January, 2017 (d) First Week of January, 2017
Fig. 5. Price prediction using SVR and CNN.
280 F. Ahmed et al.
Parameter Tuning and Cross Validation: To ﬁnd the optimal set of param-
eters for the classiﬁers, we use the deﬁned a set of parameters as shown in Table 1.
Using GS, every possible combination of parameters are tested by the propose
model to ﬁnd optimal combination of parameters.
Price Prediction: Hourly data of November and December 2016 is used to the
train classiﬁer. SVR and ECNN are used to predict price of electricity for ﬁrst
week of January. To verify the accuracy of model, predicted price is compared
with the actual price of ﬁrst week of January. The results are shown in Fig. 5(a),
(b), (c) and (d). These ﬁgures show both actual and predicted price for ﬁrst
day and ﬁrst week of January, 2017. Figure 5(a) and (b) shows the prediction of
classiﬁer SVR and Fig. 5(c) and (d) reports the result of CNN classiﬁer.
Discussion of Results: As we know, the main goal of this proposed model is
to improve the accuracy of classiﬁer to predict price correctly. The results before
parameter tuning of classiﬁer are shown in Fig. 3(a) and (b) are less accurate.
The MAE of prediction before parameter tuning is approximately equal to 2.83.
After feature selection, extraction and parameter tuning through GS, the results
are improved. The results after parameter tuning of SVR is shown in Fig. 5(a)
and (b). The results of CNN is shown in Fig. 5(c) and (d). After parameter
tuning, the accuracy of classiﬁers are improved, the MAE is reduced to 1.81 The
comparison of actual values with before and after tuning classiﬁers are shown in
Fig. 6(a) and (b). The value of MAE before tuning is 2.83 and after parameter
tuning the value is 1.81. MAE value is decreased then it shows that results are
improved after parameter tuning. Reducing 1% error values of MAE can save
thousands of MW of electricity.
(a) 2nd January, 2017 (b) First Week of January, 2017
Fig. 6. Comparison of predictions before and after parameter tunning.
A Deep Learning Approach Towards Price Forecasting Using ECNN in SG 281
In this research study, a new model is established to predict the price of elec-
tricity eﬃciently and accurately. Proposed model is consist of feature selection,
feature extraction, parameter tuning and classiﬁcation. Hybrid feature selector
(hybrid of DT and XG-boost) is used to select important features for predic-
tion. For dimensionality reduction and feature extraction, RFE is used. In order
to tune the parameters of classiﬁers, grid search is used, which boost the clas-
siﬁer’s accuracy. Enhanced classiﬁers like CNN and SVR are used to predict
price and load is proposed models for better accuracy. The results of classiﬁers
is satisfactory and show better accuracy than benchmark scheme.
1. Masip-Bruin, X., Marin-Tordera, E., Jukan, A., Ren, G.-J.: Managing resources
continuity from the edge to the cloud: architecture and performance. Future Gener.
Comput. Syst. 79, 777–785 (2018)
2. Osman, A.M.S.: A novel big data analytics framework for smart cities. Future
Gener. Comput. Syst. (2018)
3. Chen, X., Zhou, Y., Wei, D., Tang, J., Guo, Y.: Design of intelligent Demand Side
Management system respond to varieties of factors. In: 2010 China International
Conference on Electricity Distribution (CICED), pp. 1–5. IEEE (2010)
4. Jindal, A., Singh, M., Kumar, N.: Consumption-aware data analytical demand
response scheme for peak load reduction in smart grid. IEEE Trans. Industr. Elec-
5. Bilalli, B., Abell, A., Aluja-Banet, T., Wrembel, R.: Intelligent assistance for data
pre-processing. Comput. Stand. Interfaces 57, 101–109 (2018)
6. Mohsenian-Rad, A.-H., Leon-Garcia, A.: Optimal residential load control with
price prediction in real-time electricity pricing environments. IEEE Trans. Smart
Grid 1(2), 120–133 (2010)
7. Chandrashekar, G., Sahin, F.: A survey on feature selection methods. Comput.
Electr. Eng. 40(1), 16–28 (2017)
8. Erol-Kantarci, M., Mouftah, H.T.: Energy-eﬃcient information and communica-
tion infrastructures in the smart grid: a survey on interactions and open issues.
IEEE Commun. Surv. Tutorials 17(1), 179–197 (2015)
9. Wang, K., Li, H., Feng, Y., Tian, G.: Big data analytics for system stability evalu-
ation strategy in the energy Internet. IEEE Trans. Industr. Inf. 13(4), 1969–1978
10. Wang, K., Ouyang, Z., Krishnan, R., Shu, L., He, L.: A game theory-based energy
management system using price elasticity for smart grids. IEEE Trans. Industr.
Inf. 11(6), 1607–1616 (2015)
11. Kuo, P.-H., Huang, C.-J.: An electricity price forecasting model by hybrid struc-
tured deep neural networks. Sustainability 10(4), 1280 (2018)
12. Ugurlu, U., Oksuz, I., Tas, O.: Electricity price forecasting using recurrent neural
networks. Energies 11(5), 1–23 (2018)
282 F. Ahmed et al.
13. Eapen, R.R., Simon, S.P.: Performance analysis of combined similar day and day
ahead short term electrical load forecasting using sequential hybrid neural net-
works. IETE J. Res. 1–11 (2018)
14. Chitsaz, H., Zamani-Dehkordi, P., Zareipour, H., Parikh, P.: Electricity price
forecasting for operational scheduling of behind-the-meter storage systems. IEEE
Trans. Smart Grid (2017)
15. Patil, M., Deshmukh, S.R., Agrawal, R.: Electric power price forecasting using
data mining techniques. In: 2017 International Conference on Data Management,
Analytics and Innovation (ICDMAI), pp. 217–223. IEEE (2017)
16. Ziming, M.A., Zhong, H., Xie, L., Xia, Q., Kang, C.: Month ahead average daily
electricity price proﬁle forecasting based on a hybrid nonlinear regression and SVM
model: an ERCOT case study. J. Mod. Power Syst. Clean Energy 6(2), 281–291
17. Lago, J., De Ridder, F., De Schutter, B.: Forecasting spot electricity prices: deep
learning approaches and empirical comparison of traditional algorithms. Appl.
Energy 221, 386–405 (2018)
18. Wang, K., Xu, C., Zhang, Y., Guo, S., Zomaya, A.: Robust big data analytics for
electricity price forecasting in the smart grid. IEEE Trans. Big Data (2017)
19. Wang, L., Zhang, Z., Chen, J.: Short-term electricity price forecasting with stacked
denoising autoencoders. IEEE Trans. Power Syst. 32(4), 2673–2681 (2017)
20. Lago, J., De Ridder, F., Vrancx, P., De Schutter, B.: Forecasting day-ahead elec-
tricity prices in Europe: the importance of considering market integration. Appl.
Energy 211, 890–903 (2018)
21. Raviv, E., Bouwman, K.E., van Dijk, D.: Forecasting day-ahead electricity prices:
utilizing hourly prices. Energy Econ. 50, 227–239 (2015)
22. Mujeeb, S., Javaid, N., Akbar, M., Khalid, R., Nazeer, O., Khan, M.: Big data
analytics for price and load forecasting in smart grids. In: International Conference
on Broadband and Wireless Computing, Communication and Applications, pp. 77–
87. Springer, Cham (2018)
23. Raﬁei, M., Niknam, T., Khooban, M.-H.: Probabilistic forecasting of hourly elec-
tricity price by generalization of ELM for usage in improved wavelet neural net-
work. IEEE Trans. Industr. Inf. 13(1), 71–79 (2017)
24. Abedinia, O., Amjady, N., Zareipour, H.: A new feature selection technique for load
and price forecast of electrical power systems. IEEE Trans. Power Syst. 32(1), 62–
25. Ghasemi, A., Shayeghi, H., Moradzadeh, M., Nooshyar, M.: A novel hybrid algo-
rithm for electricity price and load forecasting in smart grids with demand-side
management. Appl. Energy 177, 40–59 (2016)
26. Shayeghi, H., Ghasemi, A., Moradzadeh, M., Nooshyar, M.: Simultaneous day-
ahead forecasting of electricity price and load in smart grids. Energy Convers.
Manage. 95, 371–384 (2015)
27. Keles, D., Scelle, J., Paraschiv, F., Fichtner, W.: Extended forecast methods for
day-ahead electricity spot prices applying artiﬁcial neural networks. Appl. Energy
162, 218–230 (2016)
28. Wang, J., Liu, F., Song, Y., Zhao, J.: A novel model: Dynamic Choice Artiﬁcial
Neural Network (DCANN) for an electricity price forecasting system. Appl. Soft
Comput. 48, 281–297 (2016)
A Deep Learning Approach Towards Price Forecasting Using ECNN in SG 283
29. Varshney, H., Sharma, A., Kumar, R.: A hybrid approach to price forecasting incor-
porating exogenous variables for a day ahead electricity market. In: IEEE Interna-
tional Conference on Power Electronics, Intelligent Control and Energy Systems
(ICPEICES), pp. 1–6. IEEE (2016)
30. Mosbah, H., El-Hawary, M.: Hourly electricity price forecasting for the next month
using multilayer neural network. Can. J. Electr. Comput. Eng. 39(4), 283–291
31. NYISO Hourly SMD from 2011 to 2018. https://drive.google.com/open?id=1XO-