Day-Ahead Electricity Demand Forecasting Using a Hybrid Method
Nowadays, artificial intelligence is commonly used in many fields including medicine, chemistry, and forecasting. In this paper, artificial intelligence is applied to electricity demand forecasting due to the demand for this from both providers and consumers at this time. In order to seek accurate demand forecasting methods, this article proposes a new combined electric load forecasting method (SPLSSVM), which is based on seasonal adjustment (SA) and least square support vector machine (LSSVM) optimized by the particle swarm optimization (PSO) algorithm, to forecast electricity demand. The effectiveness of SPLSSVM is tested with a dataset from New South Wales (NSW) in Australia. Experimental results demonstrate that the SPLSSVM model can offer more precise results than other methods mentioned in the literature.
KeywordsElectricity demand forecasting Particle swarm optimization Least square support vector machine
- 2.Hsu LC. Using improved grey forecasting models to forecast the output of opto-electronics industry. Expert Syst Appl. 2011;38(11):13879–85.Google Scholar
- 8.Eberhart R, Kennedy J. New optimizer using particle swarm theory. In: Proceeding of the Sixth International Symposium on Micro Machine and Human Science; IEEE, Piscataway; 1995. p. 39–43.Google Scholar