Forecasting Method of Energy Demand of Integrated Energy System Considering Seasonal Catastrophe

Abstract

Abstract The change of season will cause a variety of factors affecting energy demand to change, resulting in severe fluctuations in energy demand. Accurate prediction is of great value for energy management. Therefore, a prediction method of energy demand of integrated energy system considering seasonal mutation is proposed. Based on the analysis of the basic concepts, attributes and influencing factors of energy and energy demand, a seasonal energy demand impact decomposition model is constructed by lmdi-i to decompose the impact of season on energy demand. By dividing the energy indicators of the integrated energy system, a fuzzy neural network with error output and correction mechanism is established to predict energy demand. The test results show that the maximum relative error of the prediction results of the design method is 5.89%, the minimum relative error is 1.03%, the average absolute error is 3.21%, the root mean square error is 0.019, and the hill inequality coefficient is 0.020. Are better than the comparison method.