Efficient grid management: smart forecasting of short-term power load using PSO-LSTM

Recent load forecasting techniques combining machine learning models and hyperparameter optimization algorithms have shown success for short-term load forecasting (STLF) task, but they often require complex programming, higher computational costs, and greater parameter tuning. In this paper, we intr...

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Bibliographic Details
Published in:Engineering Research Express Vol. 6; no. 3; pp. 35364 - 35379
Main Authors: Badjan, Ansumana, Rashed, Ghamgeen Izat, Bahageel, Ahmed O M, Ali I Gony, Hashim, Shaheen, Husam I, Tuaimah, Firas Mohammed
Format: Journal Article
Language:English
Published: IOP Publishing 01.09.2024
Subjects:
hyperparameter optimization
long short-term memory
particle swarm optimization
predictive modelling
short-term load forecasting
ISSN:2631-8695, 2631-8695
Online Access:Get full text
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Summary:Recent load forecasting techniques combining machine learning models and hyperparameter optimization algorithms have shown success for short-term load forecasting (STLF) task, but they often require complex programming, higher computational costs, and greater parameter tuning. In this paper, we introduce an improved STLF model that combines Long Short-Term Memory (LSTM) neural network with Particle Swarm Optimization (PSO) for enhanced performance. In the proposed approach, the number of hidden neurons in different LSTM layers, learning rate and the number of iterations for training are optimized using the PSO algorithm. To validate the effectiveness of this method, meteorological data and historical load data from a real-world power grid are used as input. The experimental results reveal PSO significantly enhances hyperparameter tuning for LSTM neural networks, leading to improved predictive modelling. The PSO-LSTM model performed better than the LSTM model by more than 20% (in terms of Mean Absolute Error), and showed low sensitivity to hyperparameters. Comparative analysis with alternative approaches from the literature further validates the PSO-LSTM’s effectiveness in STLF. Additionally, the model achieved stable multi-step prediction capabilities, with average errors of 3.6445 for MAE, 4.6509 for RMSE, and 4.6519 for MAPE over a 1–4 day ahead lead times. This study highlights PSO-LSTM’s enhanced robustness and accuracy in power load prediction while addressing hyperparameter tuning challenges through self-optimization.
Bibliography:ERX-105466.R1
ISSN:2631-8695
2631-8695
DOI:10.1088/2631-8695/ad7ad8