Abstract
With the continuous development of smart grids, short-term power load forecasting has become increasingly important in the operation of power markets and demand-side management. In order to explore the influence of temperature and holidays on seasonal loads, this paper proposes a short-term SVM power load forecasting method based on K-Means clustering. The method includes the steps of selecting similar days, data preprocessing, SVM prediction model training and parameter adjustment. Among them, the selection of similar days uses K-Means to group seasonal load data into two categories according to temperature characteristics, as the input data to explore the effect of temperature on seasonal load. And divide the data into holidays and working days as the model input data to discover the impact of holidays on seasonal loads by using calendar rules. In order to verify the load forecasting effect of the proposed method, several experiments were carried out on two actual residential load data and two data online, and compared with the LSTM and decision tree load forecasting models in terms of prediction accuracy evaluation index and running time. The results show that the model constructed in this paper has 39.75% improved to the conventional methods for the accuracy and 128.89% improved for the running time.
Similar content being viewed by others
References
Barman M, Choudhury ND, Sutradhar S (2018) A regional hybrid GOA-SVM model based on similar day approach for short-term load forecasting in Assam, India. Energy 145:710–720
Bozkurt ÖÖ, Biricik G, Tayşi ZC (2017) Artificial neural network and Sarima based models for power load forecasting in Turkish electricity market. PLoS One 12(4):e0175915
Friedrich L, Afshari A (2015) Short-term forecasting of the Abu Dhabi electricity load using multiple weather variables. Energy Procedia 75:3014–3026
Haben S, Giasemidis G, Ziel F, Arora S (2019) Short term load forecasting and the effect of temperature at the low voltage level. Int J Forecast 35(4):1469–1484
Hafeez G, Alimgeer KS, Khan I (2020) Electric load forecasting based on deep learning and optimized by heuristic algorithm in smart grid. Appl Energy 269:114915
Herui C, Xu P (2015) Summer short-term load forecasting based on Arimax model. Power Syst Prot Control 43(4):108–114
Huang N, Wang W, Wang S, Wang J, Cai G, Zhang L (2020) Incorporating load fluctuation in feature importance profile clustering for day-ahead aggregated residential load forecasting. IEEE Access 8:25198–25209
Kong W, Dong ZY, Jia Y, Hill DJ, Xu Y, Zhang Y (2017) Short-term residential load forecasting based on LSTM recurrent neural network. IEEE Trans Smart Grid 10(1):841–851
Lee WJ, Hong J (2015) A hybrid dynamic and fuzzy time series model for mid-term power load forecasting. Int J Electr Power Energy Syst 64:1057–1062
Lee CW, Lin BY (2017) Applications of the chaotic quantum genetic algorithm with support vector regression in load forecasting. Energies 10(11):1832
Lei J, Jin T, Hao J, Li F (2019) Short-term load forecasting with clustering-regression model in distributed cluster. Clust Comput 22(4):10163–10173
Lin L, Xin W, Shengyu S (2019) Resident-side intelligent power ubiquitous sensing technology and multi-precision service research. Distrib Util 36(6):10–15
Lu H, Li Y, Chen M, Kim H, Serikawa S (2018) Brain intelligence: go beyond artificial intelligence. Mobile Netw Appl 23(2):368–375
Lu S, Lin G, Liu H, Ye C, Que H, Ding Y (2019) A weekly load data mining approach based on hidden Markov model. IEEE Access 7:34609–34619
Lu H, Zhang M, Xu X, Li Y, Shen HT (2020a) Deep fuzzy hashing network for efficient image retrieval. IEEE Trans Fuzzy Syst. https://doi.org/10.1109/TFUZZ.2020.2984991
Lu H, Zhang Y, Li Y, Jiang C, Abbas H (2020b) User-oriented virtual mobile network resource management for vehicle communications. IEEE Trans Intell Transp Syst. https://doi.org/10.1109/TITS.2020.2991766
Muzaffar S, Afshari A (2019) Short-term load forecasts using LSTM networks. Energy Procedia 158:2922–2927
Ni Q, Zhuang S, Sheng H, Kang G, Xiao J (2017) An ensemble prediction intervals approach for short-term PV power forecasting. Sol Energy 155:1072–1083
Ryu S, Noh J, Kim H (2017) Deep neural network based demand side short term load forecasting. Energies 10(1):3
Vrablecová P, Ezzeddine AB, Rozinajová V, Šárik S, Sangaiah AK (2018) Smart grid load forecasting using online support vector regression. Comput Electr Eng 65:102–117
Wang D, Luo H, Grunder O, Lin Y, Guo H (2017) Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by firefly algorithm. Appl Energy 190:390–407
Wang Z, Wang Y, Zeng R, Srinivasan RS, Ahrentzen S (2018) Random forest based hourly building energy prediction. Energy Build 171:11–25
Welikala S, Dinesh C, Ekanayake MPB, Godaliyadda RI, Ekanayake J (2017) Incorporating appliance usage patterns for non-intrusive load monitoring and load forecasting. IEEE Trans Smart Grid 10(1):448–461
Xia C, Zhang M, Cao J (2018) A hybrid application of soft computing methods with wavelet SVM and neural network to electric power load forecasting. J Electr Syst Inf Technol 5(3):681–696
Xiao L, Wang J, Hou R, Wu J (2015) A combined model based on data pre-analysis and weight coefficients optimization for electrical load forecasting. Energy 82:524–549
Yu F, Xu X (2014) A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network. Appl Energy 134:102–113
Zahid M, Ahmed F, Javaid N, Abbasi RA, Zainab Kazmi HS, Javaid A, Bilal M, Akbar M, Ilahi M (2019) Electricity price and load forecasting using enhanced convolutional neural network and enhanced support vector regression in smart grids. Electronics 8(2):122
Zhang P, Wu X, Wang X, Bi S (2015) Short-term load forecasting based on big data technologies. CSEE J Power Energy Syst 1(3):59–67
Zhang Y, Chen B, Pan G, Zhao Y (2019) A novel hybrid model based on VMD-WT and PCA-BP-RBF neural network for short-term wind speed forecasting. Energy Convers Manage 195:180–197
Zhao H, Guo S (2016) An optimized grey model for annual power load forecasting. Energy 107:272–286
Acknowledgements
We would like to thank the anonymous reviewers for their comments and constructive suggestions that have improved the paper. The subject is sponsored by the National Natural Science Foundation of P. R. China (No. 51977113,51507084), BAGUI Scholar Program of Guangxi Zhuang Autonomous Region of China (201979) and NUPTSF (No. NY219095).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Dong, X., Deng, S. & Wang, D. A short-term power load forecasting method based on k-means and SVM. J Ambient Intell Human Comput 13, 5253–5267 (2022). https://doi.org/10.1007/s12652-021-03444-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-021-03444-x