Research on integrated decision making of multiple load combination forecasting for integrated energy system

An accurate multivariate load combination forecast is essential for the planning of the energy dispatch and operation of integrated energy systems. An integrated decision model for multivariate load combination forecasting of integrated energy systems is proposed to address various problems of multivariate load combination forecasting of integrated energy systems, which is capable of evaluating the advantages and disadvantages of different forecasting methods under the same forecasting scenario. First, historical load data, meteorological factors, calendar rules, load combination indicators and integrated meteorological indicators are correlated using Pearson's correlation coefficient. Then, input features are extracted. Subsequently, four single prediction models and three combination prediction models, including a relatively novel combination prediction model of mathematical computation, were employed to predict and preliminarily analyze the electricity, cooling, and heating loads of the integrated energy system. Finally, the three loads are assigned using Best-Worst Method combined with Entropy Weight Method combinatorial weighting model, and two comprehensive evaluation indices are reconstructed and integrated decision making for the seven models is performed by considering the three loads comprehensively. The case study demonstrates that the integrated root mean square error of the adopted mathematical computational combination forecasting model in winter and summer is 7.03 and 4.71, respectively, and the integrated mean absolute percentage error is 0.94 and 1.2, respectively, which is significantly higher than that of the other models in terms of forecast accuracy.