Year: 2020
Type: article
Abstract: Load forecasting has always been a crucial part of an efficient power system planning an operation. Since the global electricity market hasdeveloped rapidly in recent years, a load sequence has gradually become non-stationary and hence makes accurate forecasting difficult.Nowadays, many techniques can be used for load forecasting such as fuzzy logic, similarday approach, expert systems, etc.. However, thesemethods normally obtain undesired results due to considerable variation of electricity load. In this paper, a short-term load forecasting model isproposed based on Long Short-Term Memory (LSTM) network combining linear regression algorithm and EVN-NLDC (National LoadDispatch Centre) data. Short-term load forecasting has an enormous impact on unit commitment, strategic power reserve for national powersystem and enhances the reliability of the power supply. National load data could be considered as a time series sequence consisting of twocomponents which are trend and residual. Linear regression (LR) is adopted for trend forecasting and LSTM is used to residual forecasting. Inaddition, to evaluate the performance of the proposed model, artificial neural network (ANN) is utilized as a benchmark model. The resultsshow that the proposed model achieves the Mean Absolute Percentage Error (MAPE) ranged from 1% to around 4% for one-day ahead loadforecasting, whereas ANN model obtains over 4%.
Institution: Hanoi University of Science and Technology
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
Funder: Trường Đại học Bách Khoa Hà Nội
OpenAlex ID: W3156705096