Accurate multi-energy load forecasting is pivotal for achieving supply–demand balance and enabling economic dispatch in Integrated energysystems (IES). However, prediction accuracy is significantly compromised by me...
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Accurate multi-energy load forecasting is pivotal for achieving supply–demand balance and enabling economic dispatch in Integrated energysystems (IES). However, prediction accuracy is significantly compromised by meteorological fluctuations, seasonal coupling variations, and multi-energy interactions affecting multi-energy loads. This study proposes a joint multi-energy load forecasting framework integrating meteorological variation rate features with multi task learning (MTL) and single task learning (STL). First, historical load variation rates and meteorological variation rates are calculated, with rapid maximal information coefficient (RapidMIC) employed to identify dominant meteorological variation rates influencing multi-load fluctuations. Subsequently, a weighted average of correlation analysis method quantifies both linear and nonlinear impacts of meteorological variation rates on load fluctuations, enabling precise screening of critical meteorological input features. Second, addressing potential accuracy degradation in MTL models caused by coupling strength disparities among multi-energy loads, a joint forecasting method based on MTL-STL is proposed to enhance prediction precision. Furthermore, a loss function optimization strategy combining homoscedastic uncertainty (HU) and dynamic weight averaging (DWA) achieves real-time weight allocation in MTL. Finally, validation through case studies on IES at Arizona State University (ASU) and an industrial park in Gansu Province, China demonstrates the model’s effectiveness and generalizability: Across all seasons, MAPE values for electricity, cool, and heating loads remain stable within 1.068%-3.022%, 1.877%-5.331%, and 1.697%-3.999% respectively.
Accompanied by energy structure transformation and the depletion of fossil fuels, large-scale distributed power sources and electric vehicles are accessed to distribution network that result in the load peak-valley ga...
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Antenna is widely used in wireless communication equipment, which is developing towards miniaturization and high frequency. The application of fractal theory in antenna design can make the antenna more miniaturized an...
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Load peak and valley periods as the key scheduling scenarios, the effectiveness of wind power prediction becomes extremely important, in order to minimize the impact of wind power on the powersystem due to prediction...
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作者：
Wei, XiuyanZou, GuibinShandong University
Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Ji'nan250061 China Northeast Electric Power University
Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Jilin132012 China
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