A novel variable weight accumulation multiple power-law grey Bernoulli model and its application in China's electricity supply and consumption prediction

The increasing demand for electricity consumption highlights the importance of scientifically and accurately predicting electricity consumption. Evaluating and predicting the patterns of electric power consumption and provision is critical for advancing the electric industry. To enhance the accuracy of power supply and electricity consumption forecasting, this paper constructs a novel variable weight accumulation multiple power-law grey Bernoulli model. Initially, a varying-weight accumulation operator was established, and it has been verified that this novel operator complies with the new information precedence principle. Then, a new model is introduced, which integrates multiple positive exponential power delay terms into the traditional Bernoulli model. The derivation of parameter estimation and time response function for the new grey model has been carried out, and the parameters have been optimized using the IGWO algorithm. The performance of the new model is validated using relevant data from three cases: China's available electricity supply, hydroelectric power generation capacity, and total electricity consumption in residential areas. This paper employs Monte Carlo simulation to generate 100 sets of results, followed by conducting a probability density analysis on these 100 sets of results, to validate the robustness of the new model and enhance the credibility of experimental outcomes. Finally, the novel model is applied to predict datasets across three scenarios from 2021 to 2025. Through analyzing electricity-related cases, the model demonstrates good predictive capability and strong adaptability.