ChapterPDF Available

Electricity Load Forecasting in Smart Grids Using Support Vector Machine

Chapter

Electricity Load Forecasting in Smart Grids Using Support Vector Machine

Abstract and Figures

One of the key issues in the Smart Grid (SG) is accurate electric load forecasting. Energy generation and consumption have highly varying. Accurate forecasting of electric load can decrease the fluctuating behavior between energy generation and consumption. By knowing the upcoming electricity load consumption, we can control the extra energy generation. To solve this issue, we have proposed a forecasting model, which consists of a two-stage process; feature engineering and classification. Feature engineering consists of feature selection and extraction. By combining Extreme Gradient Boosting (XGBoost) and Decision Tree (DT) techniques, we have proposed a hybrid feature selector to minimize the feature redundancy. Furthermore, Recursive Feature Elimination (RFE) technique is applied for dimension reduction and improve feature selection. To forecast electric load, we have applied Support Vector Machine (SVM) set tuned with three super parameters, i.e., kernel parameter, cost penalty, and incentive loss function parameter. Electricity market data is used in our proposed model. Weekly and months ahead forecasting experiments are conducted by proposed model. Forecasting performance is assessed by using RMSE and MAPE and their values are 1.682 and 12.364. The simulation results show 98% load forecasting accuracy.
Content may be subject to copyright.
Electricity Load Forecasting in Smart
Grids Using Support Vector Machine
Nasir Ayub1, Nadeem Javaid1(B
), Sana Mujeeb1, Maheen Zahid1,
Wazir Zada Khan2, and Muhammad Umar Khattak3
1COMSATS University Islamabad, Islamabad 44000, Pakistan
nadeemjavaidqau@gmail.com
2Farasan Networking Research Laboratory,
Department of Computer Science and Information System, Jazan University,
Jazan 82822-6694, Saudi Arabia
3Bahria University, Islamabad 44000, Pakistan
http://www.njavaid.com
Abstract. One of the key issues in the Smart Grid (SG) is accurate
electric load forecasting. Energy generation and consumption have highly
varying. Accurate forecasting of electric load can decrease the fluctuating
behavior between energy generation and consumption. By knowing the
upcoming electricity load consumption, we can control the extra energy
generation. To solve this issue, we have proposed a forecasting model,
which consists of a two-stage process; feature engineering and classifi-
cation. Feature engineering consists of feature selection and extraction.
By combining Extreme Gradient Boosting (XGBoost) and Decision Tree
(DT) techniques, we have proposed a hybrid feature selector to mini-
mize the feature redundancy. Furthermore, Recursive Feature Elimina-
tion (RFE) technique is applied for dimension reduction and improve
feature selection. To forecast electric load, we have applied Support Vec-
tor Machine (SVM) set tuned with three super parameters, i.e., kernel
parameter, cost penalty, and incentive loss function parameter. Electric-
ity market data is used in our proposed model. Weekly and months ahead
forecasting experiments are conducted by proposed model. Forecasting
performance is assessed by using RMSE and MAPE and their values
are 1.682 and 12.364. The simulation results show 98% load forecasting
accuracy.
1 Introduction
Smart Grid (SG) is an intelligent power system that efficiently manages gener-
ation, distribution and consumption of energy by introducing new technologies
in power grids and enable two-way communication between consumer and util-
ity [1]. Energy is the necessity and most valuable asset. The new generation is
attracted towards SG due to the extensive shortage of energy during the sum-
mer. SG manages the generation, distribution and consumption by implementing
different techniques on the power grid, utility and demand side. Efficient energy
c
Springer Nature Switzerland AG 2020
L. Barolli et al. (Eds.): AINA 2019, AISC 926, pp. 1–13, 2020.
https://doi.org/10.1007/978-3-030-15032-7_1
2 N. Ayub et al.
utilization can reduce the shortage of energy, minimize the electricity cost. Many
works are performed on different problems of SG [2]. At DSM, the home appli-
ances are scheduled using meta heuristic techniques to reduce the electricity cost,
peak to average ratio and achieve an optimal tradeoff between electricity cost
and user comfort [3].
SG facilitates consumer in reliability and sustainability by providing efficient
energy management. The Smart Meter (SM) made easy to get enough infor-
mation about future energy generation by providing real time-sharing of data
between consumer and utility. It will create a balance between energy generation
and consumption of energy. The consumer takes part in the operations of SG by
shifting the load from on peak hours to off peak hours and energy preservation
to lessen their power consumption cost [4,5].
With the help of Demand Side Management (DSM), consumers can manage
their energy utilization in an economical fashion. DSM is a program in which
consumer is able to manage their energy consumption pattern, according to the
price declared by the utility. Market competitors have more benefit from the
load forecasting. Several decisions are based on upcoming load prediction, such
as demand, supply management, power generation scheduling, reliability analysis
and maintenance planning [6].
Efficient generation and consumption of energy is another issue of the energy
sector. Utility maximization is the ultimate goal of user and utility. With the
help of accurate load forecast, energy producers will maximize their cost and
consumer will take benefit of low cost price of purchasing electricity. There is
no proper energy generation strategy in SG. To avoid extra generation, a per-
fect balance is required between the produced and consumed energy. Therefore,
precise load forecast holds more importance for market set-up management [7].
New England Control Area Independent System Operator (ISO-NE) is a
regional transmission organization, which is managed by independent system
operator. It is responsible for wholesale energy market operations. It supplies
energy to the different states of England, including Massachusetts, Maine, Con-
necticut, New Hampshire, Vermont, and Rhodes Island. The analytics in the
paper are formed on large data set of ISO NE. Price is not the only parameter
that affects the load, however, there are some other parameters that also effect
on the electrical load such as temperature, weather conditions etc.
The quantity of real world data is quite large [8]. SG data is surveyed in
detail [9]. The large amount of data provides information to utility to perform
analysis, which leads to more improvement in the markets operation planning
and management. To optimize the demand side of SG, a decision-making method
is needed. A proper decision-making results in the minimization of power loss,
reduction in the electricity cost and PAR in end user [10]. Keeping in mind these
problems, researchers mainly focus on the power scheduling problem. Different
optimization techniques are used to solve the power scheduling issue [11,12].
There is a very huge amount of electricity load data referred as big data. Big
data are very complex and large amount of data. Big data analytics make the
extraction of hidden patterns easy, market trends and other valuable information.
Electricity Load Forecasting in Smart Grids Using Support Vector Machine 3
In literature, many techniques are used for load forecasting. The size of data is
very complex and huge, which creates difficulty in the training data. Deep Neural
Network (DNN) has the computational power to handle the training of big data.
DNN has the advantage to forecast accurately and handle huge amount of data.
Many forecasting techniques are discussed in the literature. Forecasting tech-
niques can be categorized in three groups, which are data driven, classical and
artificially intelligent. To forecast, classifier based techniques are used such as
Random forest, naive bayes and ARIMA etc. Artificial intelligence techniques are
Deep Neural Network (DNN), Artificial Neural Network (ANN), Shallow Neu-
ral Network (SNN), Particle Swam Optimization (PSO) etc. Aforementioned
techniques are used for forecasting the load or price. Due to automatic feature
extraction and training processes, neural network has an advantage over other
techniques.
In paper [1315], SNN has the worst results and have over fit problem. DNN
performs better in forecasting price and load than SNN. In [16], the author
implemented Restricted Boltzmann Machine (RBM) and Rectified Linear Unit
(ReLU) for forecasting. RBM is used for data processing and training the data,
while ReLU performs load forecasting. KPCA is used for extraction of features
and DE based SVM for price forecasting in [17]. To forecast cooling load, Deep
Auto Encoders (DAE) are used [18]. DAE performs better in achieving accuracy
and learning the data. DAE is an unsupervised learning method and outperforms
in attaining the good accuracy results.
Gated Recurrent Units (GRU) technique is implemented for forecasting the
pricein[19]. In [20], Parameter Estimation Method (PEM) is applied to detect
the abnormal behavior of load. GRU beats the Long Short Term Memory
(LSTM) technique in achieving accuracy in price forecasting. Two deep neu-
ral network techniques; Convolutional Neural Network (CNN) and LSTM are
combined for forecasting load [17]. The Hybrid of LSTM and CNN outperforms
in results than CNN and LSTM separately and other several models. DNN mod-
els show better performance in achieving accuracy in the results of forecasting.
SG big data help to find the trend of load and cost. It gives help to utility in
making a demand, supply and maintenance plan, which is the basic requirement
for demand supply balance.
Feature engineering is one of the application of the classifier. Two popular
operation are used in feature engineering; selection and extraction. Several meth-
ods are used for feature engineering in electric load. In article [2124], author
study about the existing techniques of feature engineering to gain suitable fea-
tures from the data. Forecasting accuracy can be improved by the involvement
of big data.
2 Contributions
In this article, we highlight the electricity load forecasting problem. The objective
of our work is to predict the accurate electric load forecasting using electricity
load data set. To solve this problem, we have applied SVM classifier to predict
4 N. Ayub et al.
the electricity load. SVM is a classifier that divides the data into appropriate
categories by making a hyperplane between them. The SV part of the classifier
has the advantage to define the hyperplane between that classes. SVM is a
proficient method, however, the following challenges need to be answered for
better accuracy of electricity load forecasting.
High computational complexity: SVM has high computational complex-
ity and weak in processing the uncertain data [11]. In electricity load fore-
casting, redundant features in data increase the computational complexity of
SVM in its training processes and also reduces the prediction accuracy.
Hard to tune parameters: Super parameters of SVM has an effect on
the performance of SVM in forecasting. Those parameters are Cost penalty,
kernel parameter and incentive loss function. It is difficult to find the exact
values of these parameters for higher accuracy.
To address the challenges mentioned above, we have proposed a forecasting
model called Hybrid Feature Selection, Extraction and Classification (HFSEC).
The HFS part of the model is based on Hybrid XGboost and DTC, feature
extraction process based on RFE and classification is based on SVM classifier.
The proposed model implements feature engineering by selecting features regard-
ing time period and dimensionally reduced in of electricity load data features.
The hybrid feature selector uses the combination of two techniques XGboost
and DTC, rather than using one to give a selection of features. Further remov-
ing redundancy in the data, RFE is applied. The actual contribution of this
paper is:
A forecasting model is implemented to achieve accurate load forecasting by
using the big data in SG. We have integrated selection, extraction, and clas-
sification in our proposed model to solve the addressed problem.
To implement this model, a hybrid selector is proposed by the combination
of XGboost and DTC, which gives us the feature importance and feature
selection control. RFE is used to remove the redundancy from the selected
features. We have also tuned the parameters of SVM to make forecasting
accuracy better.
The forecasting performance of our proposed model performs better. The real
world electricity load data are used in this paper. Extensive simulations are
performed, which shows 98% accurate results.
3SystemModel
Our proposed system model consists of four parts: normalizing the data by pre-
processing, training the data, testing the data, an SVM classifier with tuned
parameters and forecasting load data as shown in Fig. 1.
Electricity Load Forecasting in Smart Grids Using Support Vector Machine 5
3.1 Preprocessing Data
Daily system load data are acquired from ISO-NE. The three years system load
data, i.e. January 2015 to December 2017 are used in this article. The data are
divided month wise and similar months load data, i.e. January 2015, January
2016 and January 2017, first three weeks of the month are used in the training
process and the last week for testing. All the data are arranged in the same
manner. The data is normalized with maximum values. Data is categorized into
three parts train, test and validate data.
3.2 Training and Forecasting of Data
After preprocessing of data, we obtained training, validation and testing data.
The obtained data are given to SVM for training. The SVM has three layers; the
input layer, hidden layers, and output layer. The tuning parameters, i.e. kernel
parameter set as Radial Basis Function (RBF), cost penalty and gamma values
are set to 27 and 38. These values are finalized after extensive simulations and
tuning the parameter values. The network predicts step ahead values at each time
step during the training process of SVM. The SVM acquires every arrangement
and updates the network until the preceding time step. The first training of a
network of training data is called an initial network. The initial network formed
is tested on validation data. The initial network gains a forecasted value of a
step ahead result. After gaining the forecasting results, the forecasting network
relearns and tunes the network on validation data until the forecasting errors
are reduced to a minimum value. After all, the tuned and final network is used
for load forecasting. The steps of implementing model are listed below:
1. The load data are normalized as (F/Max (F)). Load data are divided month
wise and split into categories, train, validation and test.
2. Training data are used for network training and tested on validation data.
Forecasting errors are calculated on validation data.
3. Network is tuned and validation data actual are updated with new data.
4. The network tests on the test data, and weekly ahead load and month ahead
load are forecasted. The forecasting performance evaluation is performed by
RMSE, MAPE, MAE, and MSE.
3.3 Proposed Model
The issue of the load forecasting is accuracy. Many factors affect the electricity
load and makes the classifier training difficult. To improve the accuracy, we have
proposed a network consists of a hybrid feature selector, RFE based extraction
and SVM based classifier as shown in Fig. 1. Parts of the model are listed below.
3.3.1 Hybrid Feature Selector
This section describes the feature selection process of our model. We proposed a
hybrid feature selector, by combing the XGboost, DTC and defined threshold,
6 N. Ayub et al.
i.e. µto control feature selection. HFS consists of two feature evaluators i.e.
X and D. These two evaluators calculate the feature importance separately. In
the feature selection process, the features are selected by joining the feature
importance generated by the two evaluators. Feature selection is based WXand
WD, which can be normalized by
WX=WX/max(WX),(1)
WD=WD/max(WD).(2)
Then the feature selection perform as
Fs=reserve WX[Tk]+WD[Tk]
drop, W X[Tk]+WD[Tk]µ, (3)
WB[Tk] represents the feature importance calculated by evaluator XGBoost,
WD[Tk] shows feature importance given by the DT. µis the threshold controlling
the feature selection. Features have also redundancy among them. To remove
further redundancy and dimension reduction, they sent to RFE.
3.3.2 Feature Extraction-RFE
The feature extraction process is described in this section. The features selected
by HFS are considered to have no irrelevant features, however, it contains redun-
dant features. To reduce dimension and redundancy of features, Recursive Fea-
ture Eliminator (RFE) is applied for removing redundancy. To find a suitable
low dimensional embedding, data needs non-linear mapping in electricity load
forecasting. Thus RFE is applied to reduce nonlinear dimension.
Fig. 1. System model
Electricity Load Forecasting in Smart Grids Using Support Vector Machine 7
4 Simulations
In order to simulate the performance of our proposed work, we have performed
the simulation with python. The simulator runs on the system Intel Core i3,
4GB RAM and 500GB storage. Daily electric load data of ISO New England
Control Area (ISO NECA) from January 2015 to December 2017 are taken as
input data for the simulator. Simulation results are as follows.
4.1 Feature Selection Using XGBoost and DTC
XGBoost and Decision Tree Classifier (DTC) is applied to calculate the impor-
tance of features with respect to the target, i.e. load system. In feature selection,
every feature sequence has a form as a vector. Every element of the sequence rep-
resents the feature values of different time stamps. However, our objective is to
predict the electricity load, which is named “system load” in the data. Features
that have a small effect on the target are removed. XGBoost technique finds the
importance of features, i.e. importance in numeric values and also the dimension
of features, i.e. true or false and DTC technique shows the grade of the features.
We select the features by taking hybrid of XGBoost and DTC (XGDTC). We
set a threshold for selection of features in hybrid XGDTC i.e. features having
a grade greater than 0.7 in DTC and having a false dimension in XGBoost are
selected as best features. With the increase in threshold, more features will drop
which leads to maximize the training speed and minimize the accuracy. Best
features have the highest value of importance and also have a high effect on the
target feature. The best features are then given to the forecasting engine for load
prediction. Figures 2and 3shows the XGBoost and DTC importances.
Fig. 2. Feature importance using DTC
8 N. Ayub et al.
Fig. 3. Feature importance using XGBoost
4.2 Load Forecasting
The normalized load of three years is shown in Fig.4, which shows different
variations among different days. This is the description of load data of years
2015 to 2017. Data are split into training and testing, in which training and
testing days are 822 and 274 and given to the forecasting engine for prediction.
0200 400 600 800 1000 1200
12000
14000
16000
18000
20000
22000
24000
Normalload(MW)
Days
Fig. 4. Normalize load of ISO NE January 2015 to December 2017
Electricity Load Forecasting in Smart Grids Using Support Vector Machine 9
Figure 5, describes a load of the month December 2016, December 2017,
January 2016, and January 2017.
510 15 20 25
Days
12000
14000
16000
18000
20000
22000
24000
Load (MW)
Dec 2017
Dec 2016
Jan 2017
Jan 2016
30
Fig. 5. Similar months load
There is less change in the load pattern of similar months, i.e. January 2016,
January 2017 and December 2016, December 2017. There is high variation in the
load values of different months, i.e. January 2017 and December 2017. Therefore,
first three weeks of January 2015, January 2016 up to January 2017 are used to
1 2 3 4 5 6
14500
15000
15500
16000
16500
17000
17500
18000
18500
Load (MW)
Act ual
Pr e d i c t i o n
Days
Fig. 6. January 2017 last week prediction
10 N. Ayub et al.
train the forecasting engine and test on the last week of January 2017. All the
similar months of data are trained in the same pattern.
The forecasted load of the last week of January 2017 is shown in Fig. 6.
Figure 7, illustrates the actual and forecasted load of the month December 2017.
Load forecasting of 9 months is shown in Fig.8(Table 1).
510 15 20 25 30
13000
14000
15000
16000
17000
18000
19000
20000
Load (MW)
Act ual
Pr e d i c t i o n
Days
Fig. 7. One month prediction (Dec 2017)
50 100 150 200 250 300
12000
14000
16000
18000
20000
22000
24000
Load(MW)
Actual
Prediction
Days
Fig. 8. Nine months prediction
Electricity Load Forecasting in Smart Grids Using Support Vector Machine 11
Table 1. Attributes of data
Attributes Description
DA
demand
Day-Ahead Cleared Demand, comprised of price-sensitive
and demand
RT demand Real time demand
DA LMP Day ahead local marginal price
DA EC Energy consumption of day ahead
DA CC Congestion demand of day ahead
DA MLC Day Ahead marginal loss component
RT LMP Real time location marginal price
RT CC Real time congestion component
RT EC Energy component of real time
RT MLC Real time marginal loss component
Dry bulb Dry bulb temperature for weather station corresponding to
the load zone
Dew point Dew point temperature for weather station corresponding
to the load zone
4.3 Performance Evaluation
To evaluate the performance, two evaluators are used; Root Mean Square Error
(RMSE), Mean Average Percentage Error (MAPE). MAPE has the lowest error
value, i.e., 1.682. RMSE has the highest error value, which is not a good result.
The formulas of MAPE and RMSE is given in Eqs.4and 5.
Table 2. Performance evaluators
Evaluator Value
RMSE 12.364
MAPE 1.682
MAPE =1
T
TM
tm=1
100 |Av
Fv
|(4)
RMSE =
1
T
TM
tm=1
(AvFv)2(5)
where Avis the observed test value at time tm and Fvis the forecasted value at
time tm (Table 2).
12 N. Ayub et al.
5 Conclusions and Future Work
In this work, SVM classifier is used to solve the load forecasting accuracy prob-
lem. Forecasting model is based on feature engineering and classifier adjustment.
The forecasting model consists of two stages; feature engineering and SVM clas-
sifier. A hybrid of two techniques (XGBoost and DTC) is applied for feature
selection to select the best features among features in input data. After selec-
tion of features by feature engineering, features include some redundancy. New
features are selected after removing the redundancy by using RFE technique,
which has a positive effect on SVM classifier speed and forecasting accuracy.
The performance error metrics are calculated using MAPE and RMSE. SVM
classifier is tuned with three super parameters until the accuracy is achieved.
SVM classifier has 98% accuracy. In the future, other methods can be applied
to improve forecasting accuracy.
References
1. Kailas, A., Cecchi, V., Mukherjee, A.: A survey of communications and networking
technologies for energy management in buildings and home automation. J. Comput.
Netw. Commun. 2012, 12 (2012)
2. Iqbal, Z., Javaid, N., Iqbal, S., Aslam, S., Khan, Z.A., Abdul, W., Almogren,
A., Alamri, A.: A domestic microgrid with optimized home energy management
system. Energies 11(4), 1002 (2018)
3. Iqbal, Z., Javaid, N., Mohsin, S., Akber, S., Afzal, M., Ishmanov, F.: Performance
analysis of hybridization of heuristic techniques for residential load scheduling.
Energies 11(10), 2861 (2018)
4. Rahim, M.H., Javaid, N., Shafiq, S., Iqbal, M.N., Khalid, M.U., Memon, U.U.:
Exploiting heuristic techniques for efficient energy management system in smart
grid. In: 2018 14th International Wireless Communications and Mobile Computing
Conference (IWCMC), pp. 54–59. IEEE (2018)
5. Xiang-ting, C., Yu-hui, Z., Wei, D., Jie-bin, T., Yu-xiao, G.: Design of intelli-
gent demand side management system respond to varieties of factors. In: 2010
China International Conference on Electricity Distribution (CICED), pp. 1–5.
IEEE (2010)
6. Khan, M., Javaid, N., Naseem, A., Ahmed, S., Riaz, M., Akbar, M., Ilahi, M.:
Game theoretical demand response management and short-term load forecasting
by knowledge based systems on the basis of priority index. Electronics 7(12), 431
(2018)
7. 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)
8. Wang, K., Yu, J., Yu, Y., Qian, Y., Zeng, D., Guo, S., Xiang, Y., Wu, J.: A survey
on energy internet: architecture, approach and emerging technologies. IEEE Syst.
J. (2017)
9. Jiang, H., Wang, K., Wang, Y., Gao, M., Zhang, Y.: Energy big data: a survey.
IEEE Access 4, 3844–3861 (2016)
10. Alam, M.R., Reaz, M.B.I., Ali, M.A.M.: A review of smart homes—past, present,
and future. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 42(6), 1190–1203
(2012)
Electricity Load Forecasting in Smart Grids Using Support Vector Machine 13
11. Zhu, Q., Han, Z., Ba¸sar, T.: A differential game approach to distributed demand
side management in smart grid. In: 2012 IEEE International Conference on Com-
munications (ICC), pp. 3345–3350. IEEE (2012)
12. Soares, J., Silva, M., Sousa, T., Vale, Z., Morais, H.: Distributed energy resource
short-term scheduling using signaled particle swarm optimization. Energy 42(1),
466–476 (2012)
13. Zhu, Z., Tang, J., Lambotharan, S., Chin, W.H., Fan, Z.: An integer linear pro-
gramming based optimization for home demand-side management in smart grid,
pp. 1–5 (2012)
14. Liu, J., Li, C.: The short-term power load forecasting based on sperm whale algo-
rithm and wavelet least square support vector machine with DWT-IR for feature
selection. Sustainability 9(7), 1188 (2017)
15. 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)
16. 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)
17. Ryu, S., Noh, J., Kim, H.: Deep neural network based demand side short term load
forecasting. Energies 10(1), 3 (2016)
18. Fan, C., Xiao, F., Zhao, Y.: A short-term building cooling load prediction method
using deep learning algorithms. Appl. Energy 195, 222–233 (2017)
19. Kuo, P.-H., Huang, C.-J.: An electricity price forecasting model by hybrid struc-
tured deep neural networks. Sustainability 10(4), 1280 (2018)
20. Moghaddass, R., Wang, J.: A hierarchical framework for smart grid anomaly detec-
tion using large-scale smart meter data. IEEE Trans. Smart Grid 9(6), 5820–5830
(2018)
21. Zhao, J.H., Dong, Z.Y., Li, X.: Electricity price forecasting with effective feature
preprocessing. In: IEEE Power Engineering Society General Meeting, p. 8-pp. IEEE
(2006)
22. Qiu, Z.-W.: Mutivariable mutual information based feature selection for electric-
ity price forecasting. In: 2012 International Conference on Machine Learning and
Cybernetics (ICMLC), vol. 1, pp. 168–173. IEEE (2012)
23. 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–
74 (2017)
24. Qian, H., Qiu, Z.: Feature selection using C4.5 algorithm for electricity price pre-
diction. In: 2014 International Conference on Machine Learning and Cybernetics
(ICMLC), vol. 1, pp. 175–180. IEEE (2014)
... Their model for predicting energy is validated using the Akaike Information Criterion (AIC) and Residual Sum of Squares (SSE). Ayub et al. [56] applied SVM with three parameters (kernel parameter, cost penalty, and incentive loss function parameter) on the electricity load data set. Using various machine learning methods was also reported on a real data set collected in France [57]. ...
Article
Full-text available
Peer-to-Peer (P2P) energy trading has gained much attention recently due to the advanced development of distributed energy resources. P2P enables prosumers to trade their surplus electricity and allows consumers to purchase affordable and locally produced renewable energy. Therefore, it is significant to develop solutions that are able to forecast energy consumption and generation toward better power management, thereby making renewable energy more accessible and empowering prosumers to make an informed decision on their energy management. In this paper, several models for forecasting short-term renewable energy consumption and generating are developed and discussed. Real-time energy datasets were collected from smart meters that were installed in residential premises in Western Australia. These datasets are collected from August 2018 to Apr 2019 at fine time resolution down to 5 seconds and comprise energy import from the grid, energy export to the grid, energy generation from installed rooftop PV, energy consumption in households, and outdoor temperature. Several models for forecasting short-term renewable energy consumption and generating are developed and discussed. The empirical results demonstrate the superiority of the optimised deep learning-based Long Term Short Memory (LSTM) model in forecasting both energy consumption and generation and outperforms the baseline model as well as the alternative classical and machine learning methods by a substantial margin.
... Ayub et al. [13] proposes SVM classifier to tackle the problem of load forecasting accuracy. The forecasting model is divided into two stages: feature engineering and SVM classification. ...
Article
Full-text available
Smart grids provide a unique platform to the participants of energy markets to tweak their offerings based on demand-side management. Responding quickly to the needs of the market can help to improve the reliability of the system, as well as the cost of capital investments. Electric load forecasting is important because it is used to make and run decisions about the power grid. However, people use electricity in nonlinear ways, which makes the electric load profile a complicated signal. Even though there has been a lot of research done in this field, an accurate forecasting model is still needed. In this regard, this article proposed a hybrid cross-channel-communication (C3)-enabled CNN-LSTM model for accurate load forecasting which helps decision making in smart grids. The proposed model is the combination of three different models, i.e., a C3 block to enable channel communication of a CNN (convolutional neural networks) model, two convolutional layers to extract the features and an LSTM (long short-term memory network) model for forecasting. In the proposed hybrid model, Leaky ReLu (rectified linear unit) was used as activation function instead of sigmoid. The channel communication in CNN model makes the proposed model very light and efficient. Extensive experimentation was done on electricity load data. The results show the model’s high efficiency. The proposed model shows 98.3% accuracy and 0.4560 MAPE error.
... However, an input space with redundancy and many intercorrelated features typically decreases the accuracy of the prediction model and contributes more to the over-fitting problem. To avoid overfitting, two feature selection methods, which have been proposed in the literature, including Pearson Correlation Coefficient (PCC) [27], [28] and Recursive Feature Elimination Technique (RFE) [29], [30], [31] were used to reduce the dimension of input space. ...
Article
Full-text available
This paper addresses the estimation of household communities’ overall energy usage and solar energy production, considering different prediction horizons. Forecasting the electricity demand and energy generation of communities can help enrich the information available to energy grid operators to better plan their short-term supply. Moreover, households will increasingly need to know more about their usage and generation patterns to make wiser decisions on their appliance usage and energy-trading programs. The main issues to address here are the volatility of load consumption induced by the consumption behaviour and variability in solar output influenced by solar cells specifications, several meteorological variables, and contextual factors such as time and calendar information. To address these issues, we propose a predicting approach that first considers the highly influential factors and, second, benefits from an ensemble learning method where one Gradient Boosted Regression Tree algorithm is combined with several Sequence-to-Sequence LSTM networks. We conducted experiments on a public dataset provided by the Ausgrid Australian electricity distributor collected over three years. The proposed model’s prediction performance was compared to those by contributing learners and by conventional ensembles. The obtained results have demonstrated the potential of the proposed predictor to improve short-term multi-step forecasting by providing more stable forecasts and more accurate estimations under different day types and meteorological conditions.
... The presented scheme outperforms the conventional techniques. More examples of smart grid usecases include: load forecasting [44], energy load forecasting [45], electricity load forecasting [46], smart meter measurements [47], electricity theft detection [48]. ...
... These new neural networks not only have the structural advantages of traditional neural networks, but also have some improvements in calculation efficiency and operating cost. Besides, some mature artificial intelligence algorithm models (such as support vector machines (SVM) [15][16][17][18], support vector regression (SVR) [19,20], and fuzzy logic [21,22]) that used to solve prediction problems with nonlinear characteristics, are applied to forecast electricity load. Due to the difference of the practical problems and the diversity of feature selection methods, there is no regular feature selection model method and various classifiers have different effects on the result of feature selection. ...
Article
Full-text available
This paper proposes a particle filter (PF)-based electricity load prediction method to improve the accuracy of the microgrid day-ahead scheduling. While most of the existing prediction methods assume electricity loads follow normal distributions, we consider it is a nonlinear and non-Gaussian process which is closer to the reality. To handle the nonlinear and non-Gaussian characteristics of electricity load profile, the PF-based method is implemented to improve the prediction accuracy. These load predictions are used to provide the microgrid day-ahead scheduling. The impact of load prediction error on the scheduling decision is analyzed based on actual data. Comparison results on a distribution system show that the estimation precision of electricity load based on the PF method is the highest among several conventional intelligent methods such as the Elman neural network (ENN) and support vector machine (SVM). Furthermore, the impact of the different parameter settings are analyzed for the proposed PF based load prediction. The management efficiency of microgrid is significantly improved by using the PF method.
... Generally two machine learning techniques are mostly used where the first one is for forecasting electricity price and the later one is for the energy systems. Most of the recent methods use different flavours of deep neural networks such as [10,11,12,13] as well as the other machine learning techniques methods such as Support Vector Machine (SVM) [14,15,16], Random Forest (RF) [17], Naive and Decision Tree [18] [19]. ...
Article
Full-text available
Cloud computing is rapidly taking over the information technology industry because it makes computing a lot easier without worries of buying the physical hardware needed for computations, rather, these services are hosted by companies with provide the cloud services. These companies contain a lot of computers and servers whose main source of power is electricity, hence, design and maintenance of these companies is dependent on the availability of steady and cheap electrical power supply. Cloud centers are energy-hungry. With recent spikes in electricity prices, one of the main challenges in designing and maintenance of such centers is to minimize electricity consumption of data centers and save energy. Efficient data placement and node scheduling to offload or move storage are some of the main approaches to solve these problems. In this paper, we propose an Extreme Gradient Boosting (XGBoost) model to offload or move storage, predict electricity price, and as a result reduce energy consumption costs in data centers. The performance of this method is evaluated on a real-world dataset provided by the Independent Electricity System Operator (IESO) in Ontario, Canada, to offload data storage in data centers and efficiently decrease energy consumption. The data is split into 70% training and 30% testing. We have trained our proposed model on the data and validate our model on the testing data. The results indicate that our model can predict electricity prices with a mean squared error (MSE) of 15.66 and mean absolute error (MAE) of 3.74% respectively, which can result in 25.32% cut in electricity costs. The accuracy of our proposed technique is 91% while the accuracy of benchmark algorithms RF and SVR is 89% and 88%, respectively.
... At the end, classification is performed using SVM. The proposed framework performed well, but the computational complexity of SVM high and SVM is also not good to process uncertain data [42][43][44]. The literature review shows that most authors performed forecasting with machine learning and deep learning. ...
Article
Full-text available
Electrical load forecasting provides knowledge about future consumption and generation of electricity. There is a high level of fluctuating than behavior between energy generation and consumption. Sometimes, the energy demand of the consumer becomes higher than the energy already generated and vice versa. Electricity load forecasting provides a monitoring framework for future energy generation, consumption, and making a balance between them. In this paper, we proposed a framework, in which deep learning and supervised machine learning techniques are implemented for electricity load forecasting. The three-step model is proposed, which includes: feature selection, extraction, and classification. The hybrid of Random Forest (RF) and Extreme Gradient Boosting (XGB) is used to calculate features’ importance. The average feature importance of hybrid techniques selects the most relevant and high importance features in the feature selection method. The Recursive Feature Elimination (RFE) method is used to eliminate the irrelevant features in the feature extraction method. The load forecasting is performed with Support Vector Machine (SVM) and hybrid of Gated Recurrent Unit (GRU) and Convolutional Neural Network (CNN). The meta-heuristic algorithms, i.e., Grey Wolf Optimization (GWO) and Earth Worm Optimization (EWO) are applied to tune the hyper-parameters of SVM and CNN-GRU, respectively. The accuracy of our enhanced techniques CNN-GRU-EWO and SVM-GWO is 96.33% and 90.67%, respectively. Our proposed techniques CNN-GRU-EWO and SVM-GWO perform 7% and 3% better than State Of The Art (SOTA). In the end, a comparison with SOTA techniques is performed to show the improvement of the proposed techniques. This comparison showed that the proposed technique performed well and results in the lowest performance error rates and highest accuracy rates as compared to other techniques.
Article
Full-text available
Smart Grid (S.G.) is a digitally enabled power grid with an automatic capability to control electricity and information between utility and consumer. S.G. data streams are heterogenous and possess a dynamic environment, whereas the existing machine learning methods are static and stand obsolete in such environments. Since these models cannot handle variations posed by S.G. and utilities with different generation modalities (D.G.M.), a model with adaptive features must comply with the requirements and fulfill the demand for new data, features, and modality. In this study, we considered two open sources and one real-world dataset and observed the behavior of ARIMA, ANN, and LSTM concerning changes in input parameters. It was found that no model observed the change in input parameters until it was manually introduced. It was observed that considered models experienced performance degradation and deterioration from 5 to 15% in terms of accuracy relating to parameter change. Therefore, to improve the model accuracy and adapt the parametric variations, which are dynamic in nature and evident in S.G. and D.G.M. environments. The study has proposed a novel adaptive framework to overcome the existing limitations in electrical load forecasting models.
Article
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.
Article
Full-text available
Demand Response Management (DRM) is considered one of the crucial aspects of the smart grid as it helps to lessen the production cost of electricity and utility bills. DRM becomes a fascinating research area when numerous utility companies are involved and their announced prices reflect consumer’s behavior. This paper discusses a Stackelberg game plan between consumers and utility companies for efficient energy management. For this purpose, analytical consequences (unique solution) for the Stackelberg equilibrium are derived. Besides this, this paper presents a distributed algorithm which converges for consumers and utilities. Moreover, different power consumption activities on the basis of time series are becoming a basic need for load prediction in smart grid. Load forecasting is taken as the significant concerns in the power systems and energy management with growing technology. The better precision of load forecasting minimizes the operational costs and enhances the scheduling of the power system. The literature has discussed different techniques for demand load forecasting like neural networks, fuzzy methods, Naïve Bayes, and regression based techniques. This paper presents a novel knowledge based system for short-term load forecasting. The algorithms of Affinity Propagation and Binary Firefly Algorithm are integrated in knowledge based system. Besides, the proposed system has minimum operational time as compared to other techniques used in the paper. Moreover, the precision of the proposed model is improved by a different priority index to select similar days. The similarity in climate and date proximity are considered all together in this index. Furthermore, the whole system is distributed in sub-systems (regions) to measure the consequences of temperature. Additionally, the predicted load of the entire system is evaluated by the combination of all predicted outcomes from all regions. The paper employs the proposed knowledge based system on real time data. The proposed scheme is compared with Deep Belief Network and Fuzzy Local Linear Model Tree in terms of accuracy and operational cost. In addition, the presented system outperforms other techniques used in the paper and also decreases the Mean Absolute Percentage Error (MAPE) on a yearly basis. Furthermore, the novel knowledge based system gives more efficient outcomes for demand load forecasting.
Article
Full-text available
With the emergence of the smart grid, both consumers and electricity providing companies can benefit from real-time interaction and pricing methods. In this work, a smart power system is considered, where consumers share a common energy source. Each consumer is equipped with a home energy management controller (HEMC) as scheduler and a smart meter. The HEMC keeps updating the utility with the load profile of the home. The smart meter is connected to a power grid having an advanced metering infrastructure which is responsible for two-way communication. Genetic teaching-learning based optimization, flower pollination teaching learning based optimization, flower pollination BAT and flower pollination genetic algorithm based energy consumption scheduling algorithms are proposed. These algorithms schedule the loads in order to shave the peak formation without compromising user comfort. The proposed algorithms achieve optimal energy consumption profile for the home appliances equipped with sensors to maximize the consumer benefits in a fair and efficient manner by exchanging control messages. Control messages contain energy consumption of consumer and real-time pricing information. Simulation results show that proposed algorithms reduce the peak-to-average ratio by 34.56% and help the users to reduce their energy expenses by 42.41% without compromising the comfort. The daily discomfort is reduced by 28.18%.
Article
Full-text available
Electricity price is a key influencer in the electricity market. Electricity market trades by each participant are based on electricity price. The electricity price adjusted with the change in supply and demand relationship can reflect the real value of electricity in the transaction process. However, for the power generating party, bidding strategy determines the level of profit, and the accurate prediction of electricity price could make it possible to determine a more accurate bidding price. This cannot only reduce transaction risk, but also seize opportunities in the electricity market. In order to effectively estimate electricity price, this paper proposes an electricity price forecasting system based on the combination of 2 deep neural networks, the Convolutional Neural Network (CNN) and the Long Short Term Memory (LSTM). In order to compare the overall performance of each algorithm, the Mean Absolute Error (MAE) and Root-Mean-Square error (RMSE) evaluating measures were applied in the experiments of this paper. Experiment results show that compared with other traditional machine learning methods, the prediction performance of the estimating model proposed in this paper is proven to be the best. By combining the CNN and LSTM models, the feasibility and practicality of electricity price prediction is also confirmed in this paper.
Article
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.
Article
Full-text available
Short-term power load forecasting is an important basis for the operation of integrated energy system, and the accuracy of load forecasting directly affects the economy of system operation. To improve the forecasting accuracy, this paper proposes a load forecasting system based on wavelet least square support vector machine and sperm whale algorithm. Firstly, the methods of discrete wavelet transform and inconsistency rate model (DWT-IR) are used to select the optimal features, which aims to reduce the redundancy of input vectors. Secondly, the kernel function of least square support vector machine LSSVM is replaced by wavelet kernel function for improving the nonlinear mapping ability of LSSVM. Lastly, the parameters of W-LSSVM are optimized by sperm whale algorithm, and the short-term load forecasting method of W-LSSVM-SWA is established. Additionally, the example verification results show that the proposed model outperforms other alternative methods and has a strong effectiveness and feasibility in short-term power load forecasting.
Conference Paper
In this paper, a demand side management (DSM) scheme is used to make the energy utilization efficient. The DSM scheme encourages the consumer to change the energy utilization patterns which benefits the utility. In return, consumer gets some incentives from the utility. The objectives of the proposed DSM system include: electricity bill reduction, reduced peak to average ratio (PAR), and maximization of user comfort. In the proposed system, the electrical devices are scheduled by using elephant herding optimization (EHO) and adaptive cuckoo search (ACS) algorithms. Moreover, a new algorithm named as hybrid elephant adaptive cuckoo (HEAC) is proposed which uses the features of both of the former algorithms. A comparison of these algorithms is also presented in terms of three performance parameters. The HEAC shows better performance as compared to EHO and ACS which is evident from the simulation results. Different electricity tariffs are introduced by the utility to provide incentives to the consumers. Time of use (ToU) tariff is used to make the system effective and enables the consumers to act according to the environment. The coordination can play a very important role in cost reduction as well as in user comfort maximization. The coordination is incorporated among the electrical devices by using dynamic programming (DP). Simulation results show the effectiveness of the proposed scheme in terms of electricity utilization cost, PAR reduction, and consumer comfort maximization.
Article
Electricity price forecasting is a significant part of smart grid because it makes smart grid cost efficient. Nevertheless, existing methods for price forecasting may be difficult to handle with huge price data in the grid, since the redundancy from feature selection cannot be averted and an integrated infrastructure is also lacked for coordinating the procedures in electricity price forecasting. To solve such a problem, a novel electricity price forecasting model is developed. Specifically, three modules are integrated in the proposed model. First, by merging of Random Forest (RF) and Relief-F algorithm, we propose a hybrid feature selector based on Grey Correlation Analysis (GCA) to eliminate the feature redundancy. Second, an integration of Kernel function and Principle Component Analysis (KPCA) is used in feature extraction process to realize the dimensionality reduction. Finally, to forecast price classification, we put forward a differential evolution (DE) based Support Vector Machine (SVM) classifier. Our proposed electricity price forecasting model is realized via these three parts. Numerical results show that our proposal has superior performance than other methods.
Article
Real-time monitoring and control of smart grids is critical to the enhancement of reliability and operational efficiency of power utilities. We develop a real-time anomaly detection framework, which can be built based upon smart meter data collected at the consumers’ premises. The model is designed to detect the occurrence of anomalous events and abnormal conditions at both lateral and customer levels. We propose a generative model for anomaly detection that takes into account the hierarchical structure of the network and the data collected from smart meters. We also address three challenges existing in smart grid analytics: (i) large-scale multivariate count measurements, (ii) missing points, and (iii) variable selection. We present the effectiveness of our approach with numerical experiments.
Article
Short-term building cooling load prediction is the essential foundation for many building energy management tasks, such as fault detection and diagnosis, demand-side management and control optimization. Conventional methods, which heavily rely on physical principles, have limited power in practice as their performance is subject to many physical assumptions. By contrast, data-driven methods have gained huge interests due to their flexibility in model development and the rich data available in modern buildings. The rapid development in data science has provided advanced data analytics to tackle prediction problems in a more convenient, efficient and effective way. This paper investigates the potential of one of the most promising techniques in advanced data analytics, i.e., deep learning, in predicting 24-h ahead building cooling load profiles. Deep learning refers to a collection of machine learning algorithms which are powerful in revealing nonlinear and complex patterns in big data. Deep learning can be used either in a supervised manner to develop prediction models with given inputs and output (i.e., cooling load), or in an unsupervised manner to extract meaningful features from raw data as model inputs. This study exploits the potential of deep learning in both manners, and compares its performance in cooling load prediction with typical feature extraction methods and popular prediction techniques in the building field. The results show that deep learning can enhance the performance of building cooling load prediction, especially when used in an unsupervised manner for constructing high-level features as model inputs. Using the features extracted by unsupervised deep learning as inputs for cooling load prediction can evidently enhance the prediction performance. The findings are enlightening and could bring more flexible and effective solutions for building energy predictions.