02200nas a2200253   4500000000100000000000100001008004100002260001200043653002400055653003400079653001500113653001600128653001600144100001700160700001900177700003100196700002400227245007000251856008100321300000900402490001300411520150800424022001401932       9998                            d  c06/202310aIntelligent Systems10aLong Short Term Memory (LSTM)10aSmart Grid10aTime Series10aForecasting1 aRashed Iqbal1 aHazlie Mokhlis1 aAnis Salwa Mohd Khairuddin1 aMunir Azam Muhammad00aAn Improved Deep Learning Model for Electricity Price Forecasting  uhttps://www.ijimai.org/journal/sites/default/files/2023-06/ip2023_06_001.pdf  a1-130 vIn Press3 aAccurate electricity price forecasting (EPF) is important for the purpose of bidding strategies and minimizing the risk for market participants in the competitive electricity market. Besides that, EPF becomes critically important for effective planning and efficient operation of a power system due to deregulation of electricity industry. However, accurate EPF is very challenging due to complex nonlinearity in the time series-based electricity prices. Hence, this work proposed two-fold contributions which are (1) effective time series preprocessing module to ensure feasible time-series data is fitted in the deep learning model, and (2) an improved long short-term memory (LSTM) model by incorporating linear scaled hyperbolic tangent (LiSHT) layer in the EPF. In this work, the time series pre-processing module adopted linear trend of the correlated features of electricity price series and the time series are tested by using Augmented Dickey Fuller (ADF) test method. In addition, the time series are transformed using boxcox transformation method in order to satisfy the stationarity property. Then, an improved LSTM prediction module is proposed to forecast electricity prices where LiSHT layer is adopted to optimize the parameters of the heterogeneous LSTM. This study is performed using the Australian electricity market price, load and renewable energy supply data. The experimental results obtained show that the proposed EPF framework performed better compared to previous techniques.   a1989-1660