A seasonal hybrid procedure for electricity demand forecasting in China
By Suling Zhu, Jianzhou Wang, Weigang Zhao, Jujie Wang. 2011
115 citations recorded by the source corpus.
Electricity is a special energy which is hard to store, so the electricity demand forecasting in China remains an important problem. This paper aims at developing an improved hybrid model for electricity demand in China, which takes the advantages of moving average procedure, combined method, hybrid model and adaptive particle swarm optimization algorithm, known as MA-C-WH. It is designed for making trend and seasonal adjustments which simultaneously presents the electricity demand forecasts. Four actual electricity demand time series in China power grids are selected to illustrate the proposed MA-C-WH model, and one existing seasonal autoregressive integrated moving average model (SARIMA) is selected to compare with the proposed model using the same data series. The results of popular forecasting precision indexes show that our proposed model is an effective forecasting technique for seasonal time series with nonlinear trend.
View Paper: https://www.semanticscholar.org/paper/949324a6c1bc7c7941e562a5d03ea3e1243b49e0
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Citations in the corpus
- A variance inflation factor and backward elimination based robust regression model for forecasting monthly electricity demand using climatic variables (2015, methodology): “38 Since the combination of the traditional models can utilize the advantages of individual models, the combinatorial hybrid 39 model has been used in [12] for electricity demand forecasting.”
- A Review of Electricity Demand Forecasting in Low and Middle Income Countries: The Demand Determinants and Horizons (2020, methodology): “A hybrid forecasting model of a particle swarm optimization (PSO) algorithm, with moving average processes, is introduced while addressing seasonality effects [80].”