Application of Seasonal Trend Decomposition using Loess and Long Short-Term Memory in Peak Load Forecasting Model in Tien Giang
Open Access
- 13 October 2023
- journal article
- research article
- Published by Engineering, Technology & Applied Science Research in Engineering, Technology & Applied Science Research
- Vol. 13 (5) , 11628-11634
Abstract
Daily peak load forecasting is critical for energy providers to meet the loads of grid-connected consumers. This study proposed a Seasonal Trend decomposition using Loess combined with Long Short-Term Memory (STL-LTSM) method and compared its performance on peak forecasting of electrical energy demand with Convolutional Neural Network and LSTM (CNN-LSTM), Wavenet, and the classic approaches Artificial Neural Network (ANN) and LSTM. The study evaluated the models using demand data from the power system in Tien Giang province, Vietnam, from 2020 to 2022, considering historical demand, holidays, and weather variables as input characteristics. The results showed that the proposed STL-LSTM model can predict future demand with lower Base Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Therefore, the proposed method can help energy suppliers make smart decisions and plan for future demand.Keywords
This publication has 33 references indexed in Scilit:
- A Short-Term and High-Resolution Distribution System Load Forecasting Approach Using Support Vector Regression With Hybrid Parameters OptimizationIEEE Transactions on Smart Grid, 2016
- Short-Term Load Forecasting Based on Wavelet Transform and Least Squares Support Vector Machine Optimized by Fruit Fly Optimization AlgorithmJournal of Electrical and Computer Engineering, 2015
- A hybrid short-term load forecasting with a new input selection frameworkEnergy, 2015
- Short-term load forecasting using a kernel-based support vector regression combination modelApplied Energy, 2014
- A new hybrid Modified Firefly Algorithm and Support Vector Regression model for accurate Short Term Load ForecastingExpert Systems with Applications, 2014
- Hybrid PSO–SVM method for short-term load forecasting during periods with significant temperature variations in city of BurbankApplied Soft Computing, 2014
- Short‐term load forecasting of Australian National Electricity Market by an ensemble model of extreme learning machineIET Generation, Transmission & Distribution, 2013
- Power load forecasting using support vector machine and ant colony optimizationExpert Systems with Applications, 2010
- Electric load forecasting by support vector modelApplied Mathematical Modelling, 2009
- Load Forecasting Using Support Vector Machines: A Study on EUNITE Competition 2001IEEE Transactions on Power Systems, 2004