Prediction of Whole Social Electricity Consumption in Jiangsu Province Based on Metabolic FGM (1, 1) Model

Abstract

The achievement of the carbon peaking and carbon neutrality targets requires the adjustment of the energy structure, in which the dual-carbon progress of the power industry will directly affect the realization process of the goal. In such terms, an accurate demand forecast is imperative for the government and enterprises’ decision makers to develop an optimal strategy for electric energy planning work in advance. According to the data of the whole social electricity consumption in Jiangsu Province of China from 2015 to 2019, this paper uses the improved particle swarm optimization algorithm to calculate the fractional-order r of the FGM (1, 1) model and establishes a metabolic FGM (1, 1) model to predict the whole social electricity consumption in Jiangsu Province of China from 2020 to 2023. The results show that in the next few years the whole social electricity consumption in Jiangsu Province will show a growth trend, but the growth rate will slow down generally. It can be seen that the prediction accuracy of the metabolic FGM (1, 1) model is higher than that of the GM (1, 1) and FGM (1, 1) models. In addition, the paper analyzes the reasons for the changes in the whole society electricity consumption in Jiangsu Province of China and provides support for government decision making.

Research on emergency material demand based on urgency and satisfaction under public health emergencies

“…However, GM (1,1) relies too much on the original data information and needs to fully consider the influence of new information on the prediction results in the medium and long-term prediction [ 24 ]. The metabolic GM (1,1) model replaces the old with the new based on metabolism [ 25 ]. This paper uses the Metabolism GM (1,1) model to predict the number of infected persons.…”

Predicting Spacecraft Telemetry Data by Using Grey–Markov Model with Sliding Window and Particle Swarm Optimization

“…Te target preset minimizes the mean absolute percentage error (MAPE). Te steps of the PSO algorithm are summarized as follows [25].…”

“…Te PSO method updates the particle velocity and position using the following formulae if the two ideal values are not obtained [25]:…”

“…In other words, it is impossible to make accurate forecasts using a fxed formula to handle the dynamic changes in a data set [33]. To increase forecast accuracy, the model continuously incorporates the most recent data, eliminates the most outdated data, and builds the prediction series dynamically [25]. We integrate the PGMSW model and backdrop value optimization to increase predictive performance.…”

“…Finally, Zou et al forecasted gross domestic product by using a Grey-Markov model with artifcial neural network error correction [24]. To increase prediction accuracy, the model continuously adds the most recent information, eliminates the oldest information, and dynamically builds the forecasting sequence [25].…”