ChapterPDF Available

Hourly Electricity Load Forecasting in Smart Grid Using Deep Learning Techniques


Abstract and Figures

In this paper, a Deep Learning (DL) technique is introduced to forecast the electricity load accurately. We are facing energy shortage in today’s world. So, it is the need of the hour that proper scenario should be introduced to overcome this issue. For this purpose, moving towards Smart Grids (SG) from Traditional Grids (TG) is required. Electricity load is a factor which plays a major role in forecasting. For this purpose, we proposed a model which is based on selection, extraction and classification of historical data. Grey Correlation based Random Forest (RF) and Mutual Information (MI) is performed for feature selection, Kernel Principle Component Analysis (KPCA) is used for feature extraction and enhanced Convolutional Neural Network (CNN) is used for classification. Our proposed scheme is then compared with other benchmark schemes. Simulation results proved the efficiency and accuracy of the proposed model for hourly load forecasting of one day, one week and one month.
Content may be subject to copyright.
Hourly Electricity Load Forecasting in
Smart Grid Using Deep Learning
Abdul Basit Majeed Khan1, Nadeem Javaid2(B
), Orooj Nazeer1, Maheen
Zahid2, Mariam Akbar2, and Majid Hameed Khan3
1Abasyn University Islamabad Campus, Islamabad 44000, Pakistan
2COMSATS University Islamabad, Islamabad 44000, Pakistan
3Group 3 Technology Limited, Aldridge, UK
Abstract. In this paper, a Deep Learning (DL) technique is introduced
to forecast the electricity load accurately. We are facing energy shortage
in today’s world. So, it is the need of the hour that proper scenario should
be introduced to overcome this issue. For this purpose, moving towards
Smart Grids (SG) from Traditional Grids (TG) is required. Electricity
load is a factor which plays a major role in forecasting. For this purpose,
we proposed a model which is based on selection, extraction and classi-
fication of historical data. Grey Correlation based Random Forest (RF)
and Mutual Information (MI) is performed for feature selection, Kernel
Principle Component Analysis (KPCA) is used for feature extraction and
enhanced Convolutional Neural Network (CNN) is used for classification.
Our proposed scheme is then compared with other benchmark schemes.
Simulation results proved the efficiency and accuracy of the proposed
model for hourly load forecasting of one day, one week and one month.
Keywords: Deep learning ·Smart grid ·Random forest ·Mutual
1 Introduction
Smart Grid (SG) is an advanced form of energy distribution system. Before this
advance technology, Traditional Grids (TG) are used for storage and distribu-
tion of electricity. They are always planted away from the power usage areas.
Electricity is transmitt by long transmission wires. 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 distribution, and this
may cause the loss of energy. SG is used for efficient and reliable distribution of
electricity. It is a two way communication among utility and consumer [1]. Utili-
ties and consumers, both are able to handle the operations of grid system. With
Springer Nature Switzerland AG 2020
L. Barolli et al. (Eds.): IMIS 2019, AISC 994, pp. 185–196, 2020.
186 A. B. M. Khan et al.
the efficient and smart digital structure electricity firms have better control on
power distribution. Power consumption and generation is easily monitored while
transferring from source to destination in SG. Through this technology, energy
which is generate by renewable resources can also be include in main power grid.
SG also overcome the cost of operations for utilities and low electricity cost for
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. As the
recorded data is very large; referred to big data, it is difficult to forecast the
accurate load of electricity. In this research, we use different Artificial Neural
Network (ANN) and Deep Learning (DL) based classifiers to forecast the bet-
ter 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 main objective 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 challenges regarding energy consumption and
distribution. Both the customer and distributer, want to get advantages from
the technology. This is only possible when the efficient consumption of electricity
is occurred. Load forecasting have a great impact on reducing electricity con-
sumption in SG. One of the key problem in power generation system is accurate
prediction of load. When load is predicted accurately, it helps power generators
and distributors to improve their power grid operations, and produce electric-
ity according to the demand of consumer. Moreover, when the production is
reduced, cost is also reduced. Many ANN and DL techniques, also made lot of
efforts in forecasting better accuracy of load. To solve this issue, we proposed a
DL based technique which gives better result in terms of accuracy.
1.2 Contributions
In this paper, accurate load prediction is our primary goal. For this purpose a
DL based technique is proposed. In this work, we proposed enhanced Covolutinal
Neural Network (CNN), classifier to predict electricity load. The contributions
of this paper are describes as follows:
Selection of best features using Grey Correlation Analysis (GCA) based, Ran-
dom forest (RF) and Mutual Information (MI) is made at the start of process.
Extract the important features using Kernel Principle Component Analysis
Enhanced the model by tune hyperparameters.
Predict the accurate hourly electricity load.
Improves the efficiency of model by removing overfitting problem.
Hourly Electricity Load Forecasting in Smart Grid . . . 187
2 Related Work
Electricity price and load are the most dominant 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, authors proposed
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 used for load forecasting
CNN, implemented in [2], which is further compared with ANN and Support
Vector Machine (SVM) algorithms, shows the more accurate load forecasting for
a single residential building. In this method, many CNN layers are used on old
data before implementing the final task, and then testing of method is done by
use of already labelled dataset. This paper is implemented in resolution of one-
hour data. The drawback is that this method only consider the data before the
day of forecasting and data which is unknown at the time of prediction, might
have effect on implementation of ANN, and it has a bad effect on accuracy of
load forecasting.
In [3], framework consist of ANN is used for electricity price forecasting.
Different clustering algorithms are used for feature extraction and then ANN
algorithm is applied.
Data mining is also useful to predict the electricity load in SG. Author in
[4] used a framework based on selecting the most appropriate features from
the large datasets, and then used the hybrid of Na¨ıve Bayes(NB) and KNN
classifiers to forecast accurate electricity load. The proposed model uses two
steps to forecast load; data pre-processing and load estimation. In [5], author
used Hierarchal Learning Model (HLM), to find the most dominant factors,
influenced on electric load consumption. The factors which have a lot of effect
on electricity load usage are differentiated. This method used two strategies; time
series analysis and prediction step. Data is trained on Auto RegressiveIntegrated
Moving Average (ARIMA)and distance learning models. When forecasting of
load is done, and outcomes are compared with traditional methods, then it beats
the old techniques in terms of accuracy. In time series model, iterations are not
possible because inputs are received linearly.
A hybrid of SVR and Modified Firefly Algorithm (MFA) is used to electric-
ity load forecasting, which claims to achieve best result. The proposed technique
finds better accuracy at the end of optimization [6]. For the reliable functioning of
SG, load forecasting is necessary. In [7], 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.
A hybrid of supervised and unsupervised methods; the Dynamic Choice ANN
(DCANN), gives the considerable electricity price forecasting results. Firstly we
identify bad samples from the input dataset, and choose the best input parame-
ters for obtaining better output. Un-supervised learning helps us to choose values
randomly, and then these values are trained by supervised learning model. When
188 A. B. M. Khan et al.
the result is compared to other learning models DCANN performs better. The
drawback of this technique is that when the volatility of electricity price is on
peak, in summer, autumn and spring, price forecasting becomes difficult and
the performance decreases [8]. In [9], author derives a technique, which is used
for forecasting load in green buildings. The proposed Multi Point Fuzzy Predic-
tion (MPFP) technique use fuzzy functionality with big data, collected from the
buildings to forecast electric load curves. Energy Management System (EMS)
also used to control the distribution of energy consumption devices for accu-
rate load forecasting. Electricity Information is found by controlling the load
on smart meters. By using these patterns an efficient database is created. The
MPFP forecasts the load curve and this information is given to EMS which after
examining peak load, charge or discharge storage devices to maintain the load
at same peak and reduce the cost. The accuracy of model is not satisfactory,
because data, taken for experiment is very less which caused bad accuracy.
Load forecasting is an initial need of EMS. In paper [10], Enhanced Logistic
regression (ELR), Classification and Regression Tree (CART), RFE, RF and
Grey Wolf Optimization (GWO) techniques are used for forecasting the electric-
ity load and price. This work is working well in their model. A Bayesian Network
(BN) algorithm can also give the efficient load forecasting results after finding
the dependency relationship among included variables. Efficient input data is
provides to capture better output. Efficiency of algorithm can also be checked
by multi-scale setting; using both the spatial and temporal resolutions [11].
Most of the load forecasting theories are based on the historical and calen-
dar data. In this paper author proposed a method, which based on Root Mean
Square to predict the accuracy of forecasting load of residential buildings. Differ-
ent input parameters are obtained from calendar data and historical data, and
features are extracted through proper pre-processing. This method also shows
that long dataset will cause in decreasing the forecasting accuracy. Author also
states that, one year old data is enough for accurate load forecast [12]. In [13],
authors implement a new online SVR method, which forecast load on the basis
of incoming data. The framework does not need to consider the historical data.
This method is only suitable for short term and fast load forecasting. Single out-
put is a drawback of this method. A series of data is used as input, and whenever
values are considered from different time stamps, this method would stops work-
ing. This method could be further improved by implementing the large datasets
on SVR algorithm.
Residential electricity load forecasting has been playing an important role in
SM. A long short-term memory based Recurrent Neural Network (RNN) based
forecasting, with appliance consumption sequences is proposed to implement
such volatile problem. [14]. In [15], a hybrid algorithm; Multi-Input Multi-Output
(MIMO) is used to forecast the electricity price and load, which is also a correla-
tion between price and load. Three steps are involved to forecast electricity price
and load; to make prior subsets, choose best input constraints and, at the end
forecast price and load concurrently. This method shows better forecast accu-
racy. A hybrid artificial neural network-based day-ahead load-forecasting model
Hourly Electricity Load Forecasting in Smart Grid . . . 189
for smart grids is proposed in [16]. The proposed forecasting model comprises
three modules:
a pre-processing module;
a forecast module; and
an optimization module.
In [17], electricity consumption data is gathered from the consumers’ side and
make analysis of that data for forecasting of high electricity load. Data is gath-
ered from different areas and then make analysis on the basis of appropriate
features. After analysis satisfactory results are achieved. The drawback is that
proposed model cannot capture the fast variations of the load [18]. In paper [19],
authors examine the short term price and load forecasting using different selec-
tion methods and deep learning techniques. Another framework is implemented
in [20], which is described in three steps:
Eliminate the redundant features,
Dimensionality reduction, and
Classification of price forecasting.
However, results are enough satisfactory to forecast electricity price.
The structure of our proposed model is shown in Fig. 1. It consist of four parts,
i.e., data pre-processing, feature selection, feature extraction and classification.
Fig. 1. Proposed system model
3.1 Preprocessing Data
The first step of our proposed model is to preprocess the data. We used hourly
electricity load data of ISO-NE CA market. One year hourly load data of 2017
is used in our model. We divided the data into two parts i.e. training and testing
data. Training is done on 75% of data and remainig part is used for testing. Data
is also normalized at this stage.
190 A. B. M. Khan et al.
3.2 Feature Selection
In this section, we describe the method of feature selection. RF 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. RF importances are shown in Fig. 2while MI importances are shown
in Fig. 3. We found the importances in vector form.
Fig. 2. Feature importances using RF
3.3 Feature Extraction
Feature extraction is used to reduce dimentionality, and removes redudant fea-
tures from the data. KPCA is used in our scenario to extract the best features
from data. Radial basis KPCA identifies the dimention of feature which helps
the model to perform well. Radial basis KPCA also campared with PCA and
linear kernel in the model.
3.4 Classification
In our proposed model, classification is done with ECNN, which is described as.
Enhanced CNN In machine learning, enhanced 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.Enhanced CNN contains input layer, multiple
Hourly Electricity Load Forecasting in Smart Grid . . . 191
Fig. 3. Feature importances using MI
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 two convolutional layers. The first layer consist of, 96
filters and 2 kernels and the second layer have 32 filters and 3 kernels. 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. Flatten layer is also add to make
connection between convolution and dense layer. One dense layer is used in our
network. After the network is created, we compile it using the Adam optimizer.
Mean saqure error is used as loss function and accuracy is taken as metrics.
Finally we train the model with Keras fit() function. The model trains for 155
epochs 30 batch size.
4 Simulation and Discussion
In this section, we discuss simulation results of our proposed technique in detail,
for showing productiveness and accuracy of electricity load prediction. Our model
results, summarized as follows.
4.1 Data Description and Simulation Setup
In this paper, we used the real time data of ISO New England Control Area (ISO
NE-CA), market data from January 2017 to December 2017. We consider the
192 A. B. M. Khan et al.
hourly data of each day. Our dataset is consist of 17 columns and 8,760 instances.
Each day consist of 24 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 normalize the load data through maximum
4.2 Simulation Results
After preprocess the data, we apply our techniques to get result. Results are
described in following sections.
RF and MI Based Feature Selection Feature selection plays important role
in acheieving accuracy. We apply GCA on our data to drop unimportant features.
After droping features, we splitt the data into training and testing sets. Training
set is consist of 75% instances of data while remaining instances are used for
testing. These sets are acting as a vector. After this, we apply two tehniques,
DT and MI to gain feature importances. Figure 2shows the important features
of DT technique, and Fig. 3shows the importances of MI technique. Then we
combine the results of both techniques and define a threshold value of 0.5 to
remove the features, which have low importance. Training speed and accuracy
can also be improved with increases in threshold, which drops the more feature
and gives best features.
Feature Extraction by KPCA After successful selection of feature, previous
output is used as input in this section for feature extraction. We use KPCA to
reduce dimentionality of features. Radial basis KPCA is applied on input and
compare with PCA and linear kernel principle which is shown in Fig. 4. Radial
basis KPCA gives the most representative features than other two Kernels.
Fig. 4. Feature extraction using KPCA
Hourly Electricity Load Forecasting in Smart Grid . . . 193
Load Forecasting In this section, We predict the electricity load by enhanced
CNN and compare the results with three existing classifiers. Python libraries;
Panadas, Tenseflow, Numpy and Keras are used to build the network. Sequential
model of Enhanced CNN is used, which helps network to work layer by layer.
In our scheme five layeyrs are used which are; covolution, Max-pooling, dense,
dropout and a flatten layer. Each layer have its importance which helps the
model to predict accurate electricity load. The proposed model, reduces the
computational complexity and increases accuracy efficiently, which is big issue
in prediction. Figure 5shows the comparison of our scheme with SVR, Multi-
Layer Perceptron (MLP) and CNN, where electricity load prediction of enhanced
CNN is almost near to the targeted value. Our scheme predicts better in terms
of accuracy. Our prediction is also made on the hourly load of one day, one week
and one month.
Fig. 5. Comparison with different classifiers
Hourly Load Forecasting In this section, we compared the results of hourly
pridiction with different classifiers. One day prediction is consists of 24h of day,
one week prediction consists of 168 h, and one month prediction consists of 724 h.
Our model gives better result when compared with different classifiers which is
shown in Figs. 6,7and 8.
In this work, we noticed that it is very difficult to tune the hyperparameters
of network, because if size of hyperparameter increases then its computation
power becomes time consuming and if size is reduced, it fails to produce accurate
4.3 Performance Metrics
To calculate the performance and accuracy, two evaluators i.e. Mean Square
Error (MSE) and Mean Absolute Error (MAE), are assumed. MSE scores the
lowest error value i.e., 0.2792. Other models have greater error value when com-
pared to our model, which shows that our model outperforms in term of accuracy.
Error value comparison of our model with other classifiers, is shown in Fig.9.
194 A. B. M. Khan et al.
Fig. 6. Comparison of one day forecasting
Fig. 7. Comparison of one week forecasting
Fig. 8. Comparison of one month forecasting
Hourly Electricity Load Forecasting in Smart Grid . . . 195
Fig. 9. Error value comparison of different techniques
5 Conclusion
In this paper, electricity load forecasting is done using DL techniques. The data
we used, is one year hourly data. This data is then normalized and splitt into
training and testing sets. Feature engineering is performed using three different
techniques: DT, MI and KPCA. For predicting load, a new enhanced DL tech-
nique is also proposed in this paper. The new technique is termed as Enhanced
CNN. It outperforms the other benchmark techniques in load forecasting, on the
basis of accuracy.
1. Wang, K., Xu, C., Zhang, Y., Guo, S., Zomaya, A.: Robust big data analytics for
electricity price forecasting in the smart grid. IEEE Trans. on Big Data 5(1), 34–45
2. Amarasinghe, K., Marino, D.L., Manic, M.: Deep neural networks for energy load
forecasting. In: 2017 IEEE 26th International Symposium on Industrial Electronics
(ISIE), pp. 1483–1488. IEEE (2017)
3. 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)
4. Saleh, A.I., Rabie, A.H., Abo-Al-Ez, K.M.: A data mining based load forecasting
strategy for smart electrical grids. Adv. Eng. Inform. 30(3), 422–448 (2016)
5. Zakarya, S., Abbas, H., Belal, M.: Long-term deep learning load forecasting based
on social and economic factors in the Kuwait region. J. Theor. Appl. Inf. Technol.
95(7), (2017)
6. Kavousi-Fard, A., Samet, H., Marzbani, F.: A new hybrid modified firefly algo-
rithm and support vector regression model for accurate short term load forecasting.
Expert. Syst. Appl. 41(13), 6047–6056 (2014)
7. Mujeeb, S., Javaid, N., Ilahi, M., Wadud, Z., Ishmanov, F., Afzal, M.K.: Deep long
short-term memory: a new price and load forecasting scheme for big data in smart
cities. Sustainability 11(4), 987 (2019)
196 A. B. M. Khan et al.
8. 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)
9. Chang, H.H., Chiu, W.Y., Hsieh, T.Y. (2016). Multipoint fuzzy prediction for load
forecasting in green buildings, pp. 562–567
10. Naz, A., Javed, M.U., Javaid, N., Saba, T., Alhussein, M., Aurangzeb, K.: Short-
term electric load and price forecasting using enhanced extreme learning machine
optimization in smart grids. Energies 12(5), 866 (2019)
11. Bassamzadeh, N., Ghanem, R.: Multiscale stochastic prediction of electricity
demand in smart grids using Bayesian networks. Appl. Energy 193, 369–380 (2017)
12. Lusis, P., Khalilpour, K.R., Andrew, L., Liebman, A.: Short-term residential load
forecasting: impact of calendar effects and forecast granularity. Appl. Energy 205,
654–669 (2017)
13. Vrablecov´a, P., Ezzeddine, A.B., Rozinajov´a, V., ˇ
arik, S., Sangaiah, A.K.: Smart
grid load forecasting using online support vector regression. Comput. Electr. Eng.
65, 102–117 (2018)
14. Zafar, I., Javaid, N., Iqbal, S., Aslam, S., Khan, A.Z., Abdul, W., Almogren, A.,
Alamri, A.: A Domestic Microgrid with Optimized Home Energy Management
System. Energies 11(4), 1002 (2018)
15. Shayeghi, H., Ghasemi, A., Moradzadeh, M., Nooshyar, M.: Simultaneous day-
ahead forecasting of electricity price and load in smart grids. Energy Convers.
Manag. 95, 371–384 (2015)
16. Ahmad, A., Javaid, N., Mateen, A., Awais, M., Khan, Z.: Short-term load forecast-
ing in smart grids: an intelligent modular approach. Energies 12(1), 164 (2019)
17. Jindal, A., Singh, M., Kumar, N.: Consumption-Aware Data Analytical Demand
Response Scheme for Peak Load Reduction in Smart Grid. IEEE Trans. Ind, Elec-
tron (2018)
18. Shepero, M., van der Meer, D., Munkhammar, J., Widen, J.: Residential proba-
bilistic load forecasting: a method using Gaussian process designed for electric load
data. Appl. Energy 218, 159–172 (2018)
19. Zahid, M., Ahmed, F., Javaid, N., Abbasi, R.A., Kazmi, Z., Syeda, H., Ilahi, M.:
Electricity price and load forecasting using enhanced convolutional neural net-
work and enhanced support vector regression in Smart Grids. Electronics 8(2),
122 (2019)
20. Wang, K., Xu, C., Guo, S.: Big data analytics for price forecasting in smart grids.
In: Global Communications Conference (GLOBECOM), 2016 IEEE pp 1–6. IEEE
... In [14], the authors used load data from the past two weeks to predict load for the 15 th day using LSTMs, and compared their technique with SVMs-based load forecasting. In [15], the authors used CNNs for STELF. In [16], the authors used load data from the past 60 h in the input convolutional layer of the CNNs to predict future load. ...
... chosen fromX X X iteratively to obtain differen outputs of the CNN, as given by (15). These outputs are stacked in a vector y y y. ...
Full-text available
The authors propose bagged and boosted convolutional neural networks (CNNs) and long short‐term memory (LSTM) networks, and compare their performance with the bagged and boosted traditional shallow artificial neural networks (ANNs) for short‐term electricity load forecasting. Unlike existing references that mainly compare the performance of ensemble deep learning with single deep learning and machine learning techniques, three further performance comparisons are carried out: (1) bagged CNNs and bagged LSTMs, (2) boosted CNNs and LSTMs, and (3) bagged CNNs and bagged LSTMs, and boosted CNNs and LSTMs. This allows an insight into the individual effects of ensemble learning on CNNs and LSTMs. The proposed models' inputs consist of weather and time‐related features in addition to the past load. The use of these features allows CNNs and LSTMs to estimate further complex relationship between them and the load. We implement all these methods and compare their performance on the same New England electricity load forecasting data set via statistical analysis. Effects on the forecasting performance with reduced training data are further shown. The LSTM models have the largest performance variation and are also more sensitive to a reduction in training data. In these models, boosting can improve both prediction accuracy and consistency.
... In paper [28], the researchers proposed a deep learning method to predict electricity demand by taking longterm historical dependency data, they initially used a Long Short-Term Memory network with multi-input and multi-output models based on seasonal data. The authors in [29] enhanced a CNN model using both Random Forest for feature selection and Principal Component Analysis for feature extraction to predict hourly loads up to a month. In [30], a load forecasting method was proposed to enhance the LSTM by utilizing multiple input sequences of time lags and taking in account the periodicity of the electrical load. ...
Predicting electricity consumption is not an easy task depending on many factors that affect energy consumption. Therefore, electricity utilities and governments are always searching for intelligent models to improve the accuracy of prediction and recently, deep learning becomes the most used field in prediction. In this paper, we introduce a deep learning model based on deep feedforward neural networks and Long Short-Term Memory. The deep feedforward neural networks architecture was inspired by the Inception Residual Network v2, which achieved the highest accuracy in image classification. We compared our proposed model to other recent deep learning models in two different datasets: dataset from the Distribution Network Station of Tetouan city in Morocco and dataset from the North American Utility. The proposed model achieved the smallest error of Root Mean Square Error comparing to its counterparts.
... A hybrid ANN-based day-ahead load forecasting method for smart grids is proposed to enhance the forecasting accuracy of conventional ANN [2]. In addition, a deep learning (DL) method is employed in [7] to forecast the electricity load demand precisely. ...
Full-text available
Many day-to-day operation decisions in a smart city need short term load forecasting (STLF) of its customers. STLF is a challenging task because the forecasting accuracy is affected by external factors whose relationships are usually complex and nonlinear. In this paper, a novel hybrid forecasting algorithm is proposed. The proposed hybrid forecasting method is based on locally weighted support vector regression (LWSVR) and the modified grasshopper optimization algorithm (MGOA). Obtaining the appropriate values of LWSVR parameters is vital to achieving satisfactory forecasting accuracy. Therefore, the MGOA is proposed in this paper to optimally select the LWSVR’s parameters. The proposed MGOA can be derived by presenting two modifications on the conventional GOA in which the chaotic initialization and the sigmoid decreasing criterion are employed to treat the drawbacks of the conventional GOA. Then the hybrid LWSVR-MGOA method is used to solve the STLF problem. The performance of the proposed LWSVR-MGOA method is assessed using six different real-world datasets. The results reveal that the proposed forecasting method gives a much better forecasting performance in comparison with some published forecasting methods in all cases.
Full-text available
A Smart Grid (SG) is a modernized grid to provide efficient, reliable and economic energy to the consumers. Energy is the most important resource in the world. An efficient energy distribution is required as smart devices are increasing dramatically. The forecasting of electricity consumption is supposed to be a major constituent to enhance the performance of SG. Various learning algorithms have been proposed to solve the forecasting problem. The sole purpose of this work is to predict the price and load efficiently. The first technique is Enhanced Logistic Regression (ELR) and the second technique is Enhanced Recurrent Extreme Learning Machine (ERELM). ELR is an enhanced form of Logistic Regression (LR), whereas, ERELM optimizes weights and biases using a Grey Wolf Optimizer (GWO). Classification and Regression Tree (CART), Relief-F and Recursive Feature Elimination (RFE) are used for feature selection and extraction. On the basis of selected features, classification is performed using ELR. Cross validation is done for ERELM using Monte Carlo and K-Fold methods. The simulations are performed on two different datasets. The first dataset, i.e., UMass Electric Dataset is multi-variate while the second dataset, i.e., UCI Dataset is uni-variate. The first proposed model performed better with UMass Electric Dataset than UCI Dataset and the accuracy of second model is better with UCI than UMass. The prediction accuracy is analyzed on the basis of four different performance metrics: Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Mean Square Error (MSE) and Root Mean Square Error (RMSE). The proposed techniques are then compared with four benchmark schemes. The comparison is done to verify the adaptivity of the proposed techniques. The simulation results show that the proposed techniques outperformed benchmark schemes. The proposed techniques efficiently increased the prediction accuracy of load and price. However, the computational time is increased in both scenarios. ELR achieved almost 5% better results than Convolutional Neural Network (CNN) and almost 3% than LR. While, ERELM achieved almost 6% better results than ELM and almost 5% than RELM. However, the computational time is almost 20% increased with ELR and 50% with ERELM. Scalability is also addressed for the proposed techniques using half-yearly and yearly datasets. Simulation results show that ELR gives 5% better results while, ERELM gives 6% better results when used for yearly dataset.
Full-text available
This paper focuses on analytics of an extremely large dataset of smart grid electricity price and load, which is difficult to process with conventional computational models. These data are known as energy big data. The analysis of big data divulges the deeper insights that help experts in the improvement of smart grid’s (SG) operations. Processing and extracting of meaningful information from data is a challenging task. Electricity load and price are the most influential factors in the electricity market. For improving reliability, control and management of electricity market operations, an exact estimate of the day ahead load is a substantial requirement. Energy market trade is based on price. Accurate price forecast enables energy market participants to make effective and most profitable bidding strategies. This paper proposes a deep learning-based model for the forecast of price and demand for big data using Deep Long Short-Term Memory (DLSTM). Due to the adaptive and automatic feature learning mechanism of Deep Neural Network (DNN), the processing of big data is easier with LSTM as compared to the purely data-driven methods. The proposed model was evaluated using well-known real electricity markets’ data. In this study, day and week ahead forecasting experiments were conducted for all months. Forecast performance was assessed using Mean Absolute Error (MAE) and Normalized Root Mean Square Error (NRMSE). The proposed Deep LSTM (DLSTM) method was compared to traditional Artificial Neural Network (ANN) time series forecasting methods, i.e., Nonlinear Autoregressive network with Exogenous variables (NARX) and Extreme Learning Machine (ELM). DLSTM outperformed the compared forecasting methods in terms of accuracy. Experimental results prove the efficiency of the proposed method for electricity price and load forecasting.
Full-text available
Short-Term Electricity Load Forecasting (STELF) through Data Analytics (DA) is an emerging and active research area. Forecasting about electricity load and price provides future trends and patterns of consumption. There is a loss in generation and use of electricity. So, multiple strategies are used to solve the aforementioned problems. Day-ahead electricity price and load forecasting are beneficial for both suppliers and consumers. In this paper, Deep Learning (DL) and data mining techniques are used for electricity load and price forecasting. XG-Boost (XGB), Decision Tree (DT), Recursive Feature Elimination (RFE) and Random Forest (RF) are used for feature selection and feature extraction. Enhanced Convolutional Neural Network (ECNN) and Enhanced Support Vector Regression (ESVR) are used as classifiers. Grid Search (GS) is used for tuning of the parameters of classifiers to increase their performance. The risk of over-fitting is mitigated by adding multiple layers in ECNN. Finally, the proposed models are compared with different benchmark schemes for stability analysis. The performance metrics MSE, RMSE, MAE, and MAPE are used to evaluate the performance of the proposed models. The experimental results show that the proposed models outperformed other benchmark schemes. ECNN performed well with threshold 0.08 for load forecasting. While ESVR performed better with threshold value 0.15 for price forecasting. ECNN achieved almost 2% better accuracy than CNN. Furthermore, ESVR achieved almost 1% better accuracy than the existing scheme (SVR).
Full-text available
Daily operations and planning in a smart grid require a day-ahead load forecasting of its customers. The accuracy of day-ahead load-forecasting models has a significant impact on many decisions such as scheduling of fuel purchases, system security assessment, economic scheduling of generating capacity, and planning for energy transactions. However, day-ahead load forecasting is a challenging task due to its dependence on external factors such as meteorological and exogenous variables. Furthermore, the existing day-ahead load-forecasting models enhance forecast accuracy by paying the cost of increased execution time. Aiming at improving the forecast accuracy while not paying the increased executions time cost, a hybrid artificial neural network-based day-ahead load-forecasting model for smart grids is proposed in this paper. The proposed forecasting model comprises three modules: (i) a pre-processing module; (ii) a forecast module; and (iii) an optimization module. In the first module, correlated lagged load data along with influential meteorological and exogenous variables are fed as inputs to a feature selection technique which removes irrelevant and/or redundant samples from the inputs. In the second module, a sigmoid function (activation) and a multivariate auto regressive algorithm (training) in the artificial neural network are used. The third module uses a heuristics-based optimization technique to minimize the forecast error. In the third module, our modified version of an enhanced differential evolution algorithm is used. The proposed method is validated via simulations where it is tested on the datasets of DAYTOWN (Ohio, USA) and EKPC (Kentucky, USA). In comparison to two existing day-ahead load-forecasting models, results show improved performance of the proposed model in terms of accuracy, execution time, and scalability.
Full-text available
Microgrid is a community-based power generation and distribution system that interconnects smart homes with renewable energy sources (RESs). Microgrid efficiently and economically generates power for electricity consumers and operates in both islanded and grid-connected modes. In this study, we proposed optimization schemes for reducing electricity cost and minimizing peak to average ratio(PAR) with maximum user comfort (UC) in a smart home. We considered a grid-connected microgrid for electricity generation which consists of wind turbine and photovoltaic (PV) panel. First, the problem was mathematically formulated through multiple knapsack problem (MKP) then solved by existing heuristic techniques: grey wolf optimization (GWO), binary particle swarm optimization (BPSO), genetic algorithm (GA) and wind-driven optimization (WDO). Furthermore, we also proposed three hybrid schemes for electric cost and PAR reduction: (1) hybrid of GA and WDO named WDGA; (2) hybrid ofWDO and GWO named WDGWO; and (3) WBPSO, which is the hybrid of BPSO and WDO. In addition, a battery bank system (BBS) was also integrated to make our proposed schemes more cost-efficient and reliable, and to ensure stable grid operation. Finally, simulations were performed to verify our proposed schemes. Results show that our proposed scheme efficiently minimizes the electricity cost and PAR. Moreover, our proposed techniques, WDGA, WDGWO and WBPSO, outperform the existing heuristic techniques.
The ever-increasing load demand of the residential sector gives rise to concerns such as-decreased quality of service and increased demand-supply gap in the electricity market. To tackle these concerns, the utilities are switching to smart grids (SGs) to manage the demand response (DR) of the connected loads. However, most of the existing DR management schemes have not explored the concept of data analytics for reducing peak load while taking consumer constraints into account. To address this issue, a novel data analytical demand response (DADR) management scheme for residential load is proposed in this paper with an aim to reduce the peak load demand. The proposed scheme is primarily based on the analysis of consumers' consumption data gathered from smart homes (SHs) for which factors such as-appliance adjustment factor, appliance priority index, etc. have been considered. Based on these factors, different algorithms with respect to consumer's and utility's perspective have been proposed to take DR. In addition to it, an incentive scheme has also been presented to increase the consumers' participation in the proposed scheme. The results obtained show that it efficiently reduces the peak load at the grid by a great extent. Moreover, it also increases the savings of the consumers by reducing their overall electricity bills.
Abstract Probabilistic load forecasting (PLF) is of important value to grid operators, retail companies, demand response aggregators, customers, and electricity market bidders. Gaussian processes (GPs) appear to be one of the promising methods for providing probabilistic forecasts. In this paper, the log-normal process (LP) is newly introduced and compared to the conventional GP. The LP is especially designed for positive data like residential load forecasting—little regard was taken to address this issue previously. In this work, probabilisitic and deterministic error metrics were evaluated for the two methods. In addition, several kernels were compared. Each kernel encodes a different relationship between inputs. The results showed that the LP produced sharper forecasts compared with the conventional GP. Both methods produced comparable results to existing PLF methods in the literature. The LP could achieve as good mean absolute error (MAE), root mean square error (RMSE), prediction interval normalized average width (PINAW) and prediction interval coverage probability (PICP) as 2.4%, 4.5%, 13%, 82%, respectively evaluated on the normalized load data.
Load forecasting (LF) is a technique used by energy-providing companies to predict the power needed. LF is of great importance for ensuring sufficient capacity and manipulating the deregulation of the power industry in many countries, such as Arab gulf countries. Moreover, reduction of load forecasting error leads to lower costs and could save billions of dollars. Recently, further improvement has been introduced using more complex models that take into account dependencies among hidden layers. Also, many approach based model are presented, but all of them have limitations prediction capabilities. The purpose of this work is to demonstrate the load forecasting classes and factors impacting its performance, especially in Kuwaiti region in Arab Gulf. This work presents a novel deep leaning model that involves generating more accurate predictions for the electric load based on hierarchal learning architecture. It is integrates the features of data in discovering most influent factors affecting electrical load usage. The dataset used is the actual data from Ministry of Electrical in Kuwait, the data for load is in mega-watt long-term for the years 2006 to year 2015, which is trained using ARIMA and neural networks models. The load forecasting is done for the year 2016 and is validated for the accuracy and less for error rate. Results indicate that this architecture performs quite well when compared to traditional approaches and deep neural network.
Literature is rich in methodologies for “aggregated” load forecasting which has helped electricity network operators and retailers in optimal planning and scheduling. The recent increase in the uptake of distributed generation and storage systems has generated new demand for “disaggregated” load forecasting for a single-customer or even down at an appliance level. Access to high resolution data from smart meters has enabled the research community to assess conventional load forecasting techniques and develop new forecasting strategies suitable for demand-side disaggregated loads. This paper studies how calendar effects, forecasting granularity and the length of the training set affect the accuracy of a day-ahead load forecast for residential customers. Root mean square error (RMSE) and normalized RMSE were used as forecast error metrics. Regression trees, neural networks, and support vector regression yielded similar average RMSE results, but statistical analysis showed that regression trees technique is significantly better. The use of historical load profiles with daily and weekly seasonality, combined with weather data, leaves the explicit calendar effects a very low predictive power. In the setting studied here, it was shown that forecast errors can be reduced by using a coarser forecast granularity. It was also found that one year of historical data is sufficient to develop a load forecast model for residential customers as a further increase in training dataset has a marginal benefit.