Abstract
Integrated energy services will have multiple values and far-reaching significance in promoting energy transformation and serving “carbon peak and carbon neutralization”. In order to balance the supply and demand of power system in integrated energy, it is necessary to establish a scientific model for power load forecasting. Different algorithms for short-term electric load forecasting considering meteorological factors are presented in this paper. The correlation between electric load and meteorological factors is first analyzed. After the principal component analysis (PCA) of meteorological factors and autocorrelation analysis of the electric load, the daily load forecasting model is established by optimal support vector machine (OPT-SVM), Elman neural network (ENN), as well as their combinations through linear weighted average, geometric weighted average, and harmonic weighted average method, respectively. Based on the actual data of an industrial park of Nantong in China, the prediction performance in the four seasons with the different models is evaluated. The main contribution of this paper is to compare the effectiveness of different models for short-term electric load forecasting and to give a guideline to build the proper methods for load forecasting.
Highlights
Integrated energy services will have multiple values and far-reaching significance in promoting energy transformation and serving “carbon peak and carbon neutralization”.Before the implementation of integrated energy services, in order to balance the supply and demand of the power system, it is necessary to establish a scientific model for power load forecasting
The innovation of our paper is to find out a time-varying weight combination model which takes the results of two single models of optimal support vector machine (OPT-SVM) and Elman NN as input, studies the time history changes of each single model’s contribution to the combined forecasting, and uses statistics methods to analyze the effectiveness of the combined model, providing a useful reference for deepening the application of time-varying weight combination models in short-term load forecasting
The other rows in the figure are the correlations between various meteorological factors
Summary
Integrated energy services will have multiple values and far-reaching significance in promoting energy transformation and serving “carbon peak and carbon neutralization”. By coupling a simulated annealing particle swarm optimization algorithm and support vector machine, an optimized support vector machine model is constructed to study the short-term load forecasting effect under different meteorological variable input conditions and analyze the influence of meteorological factors on load forecasting. This paper presents some combined models for short-term load forecasting It integrates simulated annealing OPT-SVM and ENN through time-varying weights selection. The innovation of our paper is to find out a time-varying weight combination model which takes the results of two single models of OPT-SVM and Elman NN as input, studies the time history changes of each single model’s contribution to the combined forecasting, and uses statistics methods to analyze the effectiveness of the combined model, providing a useful reference for deepening the application of time-varying weight combination models in short-term load forecasting.
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