Advanced Machine Learning Solutions for Power Load Forecasting and Power Grid Planning Optimization
DOI:
https://doi.org/10.13052/dgaej2156-3306.4023Keywords:
New power system, load forecasting, power balance, machine learningAbstract
As new power systems are being developed and the pursuit of carbon neutrality intensifies, the existing power grid encounters dual uncertainty stemming from the extensive integration of renewable energy and fast load expansion. Precisely forecasting power demand and enhancing power grid planning have emerged as critical concerns for guaranteeing a secure and stable energy supply. This research presents a sophisticated deep learning approach for power load forecasting and optimization of power grid planning. Initially, deep neural networks (DNN) and long short-term memory (LSTM) models are employed to analyze historical load data and account for fluctuations in power load across several dimensions, including meteorological conditions, seasonal influences, and vacations, in order to achieve precise predictions of future loads. Subsequently, informed by the forecast findings, the grid’s power balance is modified to optimize the dependability and economic efficiency of the power supply. Experimental findings indicate that the suggested LSTM-CNN hybrid model achieves a root mean square error (RMSE) of 45.2 MW and a mean absolute percentage error (MAPE) of 1.8%, significantly outperforming the traditional GA-BP model (RMSE: 68.7 MW, MAPE: 2.9%). Specifically, the LSTM-CNN model reduces RMSE and MAPE by 34.2% and 37.9%, respectively, compared to GA-BP. This improvement demonstrates the model’s superior capability in capturing temporal and spatial patterns of power load, thereby markedly enhancing the efficiency and stability of power grid planning optimization.
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References
Li Mingjie, Liang Zhifeng, Xu Tao, et al. Prediction and application of maximum daily load in summer and winter based on spatial distribution of sensitive temperatures. Power System Technology, 2023, 47(3): 1088–1097.
Shi Jiaqi, Ma Liya, Li Chenchen, et al. Peak load forecasting method based on serial-parallel integrated learning. Proceedings of the CSEE, 2020, 40(14): 4463–4472+4726.
Liu Xing, Wang Qiuchen, Wen Yunhao, et al. Natural gas treatment plant load factor prediction model based on BO-LSTM. Natural Gas and Oil, 2023, 41(05): 122–130.
Tan Zhongfu, Pu Lei, Wu Jing, et al. Time-of-use transmission and distribution price optimization model based on differential loading rate. Systems Engineering – Theory & Practice, 2019, 39(11): 2945–2952.
Dai Haonan, Zhang Chenhao, Zhen Zhao, et al. Short-term net load forecasting based on spatio-temporal feature clustering and two-layer dynamic graph convolutional network modeling. High Voltage Engineering, 1–11 [2024-09-06].
Zhang Q, Zhou S, Xu B, et al. TCAMS-Trans: Efficient temporal-channel attention multi-scale transformer for net load forecasting. Computers and Electrical Engineering, 2024, 118(PB): 109415–109415.
Rizi T E, Rastegar M, Forootani A. Power system flexibility analysis using net-load forecasting based on deep learning considering distributed energy sources and electric vehicles. Computers and Electrical Engineering, 2024, 117: 109305.
Zhou Siming, Duan Jinchang, Li Yingjie, et al. An improved SVM short-term power system load forecasting method. Journal of Shenyang University of Technology, 2023, 45(06): 661–665.
Wang Yongli, Liu Zeqiang, Dong Huanran, et al. Short-term load forecasting of integrated energy system based on VMD-CSO-RF. Journal of North China Electric Power University (Natural Science Edition), 1–10 [2024-09-06].
Anping W, Qing C, Khalil A, et al. Short-term power load forecasting for combined heat and power using CNN-LSTM enhanced by attention mechanism. Energy, 2023, 282.
Han Yuchao, Tong Qianqian, Deng Yaping. Probability-based density estimation and time-series Transformer network for day-ahead interval prediction of wind power. Proceedings of the CSEE, 1-11 [2024-09-06].
Liu H, Li Z, Li C, et al. Research and application of short-term load forecasting based on CEEMDAN-LSTM modeling. Energy Reports, 2024, 122: 144–2155.
Liu Jie, Zhou Bowen, Tian Ming, et al. Short-term load forecasting based on migration learning and TCN-BIGRU. Journal of Beijing University of Aeronautics and Astronautics, 1–12 [2024-09-14].
Xu Gang, Pan Qichao. Research on township power grid planning based on power demand forecasting. Power Equipment Management, 2024, (19): 247–249.
Wang Tao, Zhang Zhijing. Design of a decision support system for long-term planning of a new type of power system based on renewable energy integration. Automation Application, 2025, 66(01): 128–130. DOI:nolinkurl10.19769/j.zdhy.2025.01.036.
Zhang Yan, Zuo Ya, LI Xiangping, et al. Multi-parameter grid planning method considering flexible load and node reliability demand. Automation Technology and Applications, 1–5, 2025.02.06.
Lin Chaobin, Wang Yanjie, Li Qingyao. Research on power grid planning evaluation system and practice under the background of a new type of power system. Electrical Engineering Technology, 2024, (23): 51–54. DOI:nolinkurl10.19768/j.cnki.dgjs.2024.23.013.
