Application of ALO-ELM on Electricity Demand Forecasting Under Spot Power Market

EasyChair Preprint 8376

Abstract

The electricity demand of power system and power market change with the development of economy, short-term electricity demand forecasting play a very important role in spot power Market. In this paper, the Ant Lion Optimizer (ALO) is introduced to improve the input weights and hidden-layer Matrix of extreme learning machine (ELM), after the parameters of ELM are optimized by ALO, then input nodes, hidden layer nodes and output nodes are determined, so an electricity demand forecasting model based on ALO-ELM combined algorithm is established. The proposed method is illustrated based on the historical load data of a city in China. The results show that the average absolute error of short-term load demand predicted by ALO-ELM model is 1.41, while that predicted by ELM is 4.34, it has shown than ALO-ELM algorithm is superior to the ELM and meet the requirements of engineering accuracy. Under the spot power market, accurately predicting the electricity demand trend and reducing the deviation of electricity expenditure have become an important means for the market players to make profits.

Keyphrases: Ant Lion Optimizer (ALO), Electricity demand forecasting, Extreme Learning Machine (ELM), model, parameter optimization, power market

@booklet{EasyChair:8376,
  author    = {Yan Shi and Wenzhe Zhang and Fumin Sang and Lei Zhao and Tao Wang},
  title     = {Application of ALO-ELM on Electricity Demand Forecasting Under Spot Power Market},
  howpublished = {EasyChair Preprint 8376},
  year      = {EasyChair, 2022}}