research-article

Data driven day-ahead electrical load forecasting through repeated wavelet transform assisted SVM model

Authors:

Abheejeet MohapatraAuthors Info & Claims

Volume 111, Issue C

https://doi.org/10.1016/j.asoc.2021.107730

Published: 01 November 2021 Publication History

Abstract

Electrical load forecasting is an integral tool used by the grid operator to operate the smart power network. The information related to the electrical load is a prerequisite towards the effective and optimal operation of the power network with renewable and conventional generation resources. Economical bidding in the energy markets is directing research towards a superior forecasting model. Statistical and machine learning models have been used for electrical load forecasting while considering the electrical load as a time-series signal. This paper proposes a hybrid model that combines the Wavelet Transform (WT) and Support Vector Machine (SVM) features in estimating a regression model for electrical load forecasting utilizing the historical time-series information of electrical load. The WT decomposes the electrical load time-series data into various sub-series. The error contribution in forecasting due to the individual sub-series is estimated using Mean Absolute Error (MAE) in forecasting for each sub-series. The proposed Repeated WT-based SVM model (RWT-SVM) selects the sub-series with the highest MAE for further decomposition through WT. This results in a better forecasting model for the sub-series with the highest MAE, thereby improving the overall forecasting ability of the RWT-SVM model. The superiority of the proposed Repeated WT-based SVM model (RWT-SVM) for electrical load forecasting is justified using various data sets and comparing with some of the existing forecasting models.

Highlights

•

A new Repeated WT based SVM (RWT-SVM) model is proposed for the forecasting of the day-ahead electrical load.

•

The mean absolute error for each decomposition level is different and is the reference for selection of the decomposition level for RWT.

•

RWT of the selected decomposition level leads to reduced MAE for that particular level and thus overall forecasting error is reduced.

References

[1]

Kuster C., Rezgui Y., Mourshed M., Electrical load forecasting models: A critical systematic review, Sustainable Cities Soc. 35 (2017) 257–270.

[2]

Khuntia S.R., Rueda J.L., van der Meijden M.A.M.M., Forecasting the load of electrical power systems in mid- and long-term horizons: a review, IET Gener. Transm. Distrib. 10 (16) (2016) 3971–3977,.

[3]

Hysa A., Modeling and simulation of the photovoltaic cells for different values of physical and environmental parameters, Emerg. Sci. J. 3 (6) (2019) 395–406.

[4]

Hong T., Fan S., Probabilistic electric load forecasting: A tutorial review, Int. J. Forecast. 32 (3) (2016) 914–938.

[5]

Hagan M.T., Behr S.M., The time series approach to short term load forecasting, IEEE Trans. Power Syst. 2 (3) (1987) 785–791,.

[6]

Amjady N., Short-term hourly load forecasting using time-series modeling with peak load estimation capability, IEEE Trans. Power Syst. 16 (3) (2001) 498–505,.

[7]

Lee W.-J., Hong J., A hybrid dynamic and fuzzy time series model for mid-term power load forecasting, Int. J. Electr. Power Energy Syst. 64 (2015) 1057–1062.

[8]

Chen J.-F., Wang W.-M., Huang C.-M., Analysis of an adaptive time-series autoregressive moving-average (ARMA) model for short-term load forecasting, Electr. Power Syst. Res. 34 (3) (1995) 187–196.

[9]

Ghorbani S., Barari M., Hosseini M., A modern method to improve of detecting and categorizing mechanism for micro seismic events data using boost learning system, Civ. Eng. J. 3 (9) (2017) 715–726.

[10]

Duraku R.K., Ramadani R., Development of traffic volume forecasting using multiple regression analysis and artificial neural network, Civ. Eng. J. 5 (8) (2019) 1698–1713.

[11]

Damuluri S., Islam K., Ahmadi P., Qureshi N.S., Analyzing navigational data and predicting student grades using support vector machine, Emerg. Sci. J. 4 (4) (2020) 243–252.

[12]

Raza M.Q., Nadarajah M., Hung D.Q., Baharudin Z., An intelligent hybrid short-term load forecasting model for smart power grids, Sustainable Cities Soc. 31 (2017) 264–275.

[13]

Khwaja A., Zhang X., Anpalagan A., Venkatesh B., Boosted neural networks for improved short-term electric load forecasting, Electr. Power Syst. Res. 143 (2017) 431–437.

[14]

Mansoor M., Grimaccia F., Leva S., Mussetta M., Comparison of echo state network and feed-forward neural networks in electrical load forecasting for demand response programs, Math. Comput. Simulation (2020),.

[15]

Hu R., Wen S., Zeng Z., Huang T., A short-term power load forecasting model based on the generalized regression neural network with decreasing step fruit fly optimization algorithm, Neurocomputing 221 (2017) 24–31,.

Digital Library

[16]

Yang Y., Che J., Deng C., Li L., Sequential grid approach based support vector regression for short-term electric load forecasting, Appl. Energy 238 (2019) 1010–1021,.

[17]

Chen Y., Xu P., Chu Y., Li W., Wu Y., Ni L., Bao Y., Wang K., Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings, Appl. Energy 195 (2017) 659–670,.

[18]

Yang Y., Che J., Li Y., Zhao Y., Zhu S., An incremental electric load forecasting model based on support vector regression, Energy 113 (2016) 796–808,.

[19]

Zhang Z., Hong W.-C., Electric load forecasting by complete ensemble empirical mode decomposition adaptive noise and support vector regression with quantum-based dragonfly algorithm, Nonlinear Dynam. 98 (2) (2019) 1107–1136.

[20]

Yaslan Y., Bican B., Empirical mode decomposition based denoising method with support vector regression for time series prediction: A case study for electricity load forecasting, Measurement 103 (2017) 52–61.

[21]

Chen Y., Yang Y., Liu C., Li C., Li L., A hybrid application algorithm based on the support vector machine and artificial intelligence: An example of electric load forecasting, Appl. Math. Model. 39 (9) (2015) 2617–2632.

[22]

He F., Zhou J., Feng Z.-k., Liu G., Yang Y., A hybrid short-term load forecasting model based on variational mode decomposition and long short-term memory networks considering relevant factors with Bayesian optimization algorithm, Appl. Energy 237 (2019) 103–116.

[23]

Qiu X., Ren Y., Suganthan P.N., Amaratunga G.A., Empirical mode decomposition based ensemble deep learning for load demand time series forecasting, Appl. Soft Comput. 54 (2017) 246–255.

Digital Library

[24]

Hamidian D., Salajegheh J., Salajegheh E., Damage detection of irregular plates and regular dams by wavelet transform combined adoptive neuro fuzzy inference system, Civ. Eng. J. 4 (2) (2018) 305–319.

[25]

Pandey A.S., Singh D., Sinha S.K., Intelligent hybrid wavelet models for short-term load forecasting, IEEE Trans. Power Syst. 25 (3) (2010) 1266–1273,.

[26]

Li B., Zhang J., He Y., Wang Y., Short-term load-forecasting method based on wavelet decomposition with second-order gray neural network model combined with ADF test, IEEE Access 5 (2017) 16324–16331,.

[27]

Abdoos A., Hemmati M., Abdoos A.A., Short term load forecasting using a hybrid intelligent method, Knowl.-Based Syst. 76 (2015) 139–147,.

Digital Library

[28]

Zhang X., Wang J., A novel decomposition-ensemble model for forecasting short-term load-time series with multiple seasonal patterns, Appl. Soft Comput. 65 (2018) 478–494,.

Digital Library

[29]

Sudheer G., Suseelatha A., Short term load forecasting using wavelet transform combined with Holt–Winters and weighted nearest neighbor models, Int. J. Electr. Power Energy Syst. 64 (2015) 340–346.

[30]

El-Hendawi M., Wang Z., An ensemble method of full wavelet packet transform and neural network for short term electrical load forecasting, Electr. Power Syst. Res. 182 (2020).

[31]

Bento P., Pombo J., Calado M., Mariano S., Optimization of neural network with wavelet transform and improved data selection using bat algorithm for short-term load forecasting, Neurocomputing 358 (2019) 53–71.

Digital Library

[32]

Alipour M., Aghaei J., Norouzi M., Niknam T., Hashemi S., Lehtonen M., A novel electrical net-load forecasting model based on deep neural networks and wavelet transform integration, Energy 205 (2020).

[33]

Percival D.B., Walden A.T., Wavelet Methods for Time Series Analysis, Vol. 4, Cambridge university press, 2000.

[34]

Smola A.J., Schölkopf B., A tutorial on support vector regression, Stat. Comput. 14 (3) (2004) 199–222.

Digital Library

[35]

Agrawal A.K., Chakraborty G., On the use of acquisition function-based Bayesian optimization method to efficiently tune SVM hyperparameters for structural damage detection, Struct. Control Health Monit. 28 (4) (2021).

[36]

Eseye A.T., Zhang J., Zheng D., Short-term photovoltaic solar power forecasting using a hybrid Wavelet-PSO-SVM model based on SCADA and Meteorological information, Renew. Energy 118 (2018) 357–367.

[37]

ENTSO-E, European Network of Transmission System Operators for Electricity. URL https://www.entsoe.eu/data/data-portal/.

[38]

Aasim A.T., Singh S., Mohapatra A., Repeated wavelet transform based ARIMA model for very short-term wind speed forecasting, Renew. Energy 136 (2019) 758–768,.

[39]

Dumas J., Cornélusse B., Classification of load forecasting studies by forecasting problem to select load forecasting techniques and methodologies, 2018, arXiv preprint arXiv:1901.05052.

Cited By

Xu YLi JDong YDu P(2024)Power load combination forecasting system based on longitudinal data selectionEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107629130:COnline publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1016/j.engappai.2023.107629
Fan GLi JPeng LHuang HHong W(2024)The bi-long short-term memory based on multiscale and mesoscale feature extraction for electric load forecastingApplied Soft Computing10.1016/j.asoc.2024.111853162:COnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.asoc.2024.111853
Yin LMo N(2024)Fractional-order Q-learning based on modal decomposition and convolutional neural networks for voltage control of smart gridsApplied Soft Computing10.1016/j.asoc.2024.111825162:COnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.asoc.2024.111825
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Data driven day-ahead electrical load forecasting through repeated wavelet transform assisted SVM model

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Published In

cover image Applied Soft Computing

Applied Soft Computing Volume 111, Issue C

Nov 2021

1286 pages

ISSN:1568-4946

Issue’s Table of Contents

Elsevier B.V.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 November 2021

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Cited By

Xu YLi JDong YDu P(2024)Power load combination forecasting system based on longitudinal data selectionEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107629130:COnline publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1016/j.engappai.2023.107629
Fan GLi JPeng LHuang HHong W(2024)The bi-long short-term memory based on multiscale and mesoscale feature extraction for electric load forecastingApplied Soft Computing10.1016/j.asoc.2024.111853162:COnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.asoc.2024.111853
Yin LMo N(2024)Fractional-order Q-learning based on modal decomposition and convolutional neural networks for voltage control of smart gridsApplied Soft Computing10.1016/j.asoc.2024.111825162:COnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.asoc.2024.111825
Giacomazzi EHaag FHopf K(2023)Short-Term Electricity Load Forecasting Using the Temporal Fusion Transformer: Effect of Grid Hierarchies and Data SourcesProceedings of the 14th ACM International Conference on Future Energy Systems10.1145/3575813.3597345(353-360)Online publication date: 20-Jun-2023
https://dl.acm.org/doi/10.1145/3575813.3597345
Lu YWang G(2023)A load forecasting model based on support vector regression with whale optimization algorithmMultimedia Tools and Applications10.1007/s11042-022-13462-282:7(9939-9959)Online publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1007/s11042-022-13462-2
Song X(2022)A Faster Time Series Data Prediction Method Based on LSTMProceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering10.1145/3573428.3573447(105-108)Online publication date: 21-Oct-2022
https://dl.acm.org/doi/10.1145/3573428.3573447
Shi XHuang GHao XYang YLi Z(2022)Sliding window and dual-channel CNN (SWDC-CNN)Applied Soft Computing10.1016/j.asoc.2022.109520129:COnline publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1016/j.asoc.2022.109520
Nguyen TPhan QNguyen VPham H(2021)Day-ahead electricity load forecasting based on hybrid model of EEMD and Bidirectional LSTMProceedings of the 5th International Conference on Future Networks and Distributed Systems10.1145/3508072.3508079(31-41)Online publication date: 15-Dec-2021
https://dl.acm.org/doi/10.1145/3508072.3508079

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