Daily peak demand forecasting using Pelican Algorithm optimised Support Vector Machine (POA-SVM)

Abstract

The knowledge of daily peak load consumption is crucial for energy planning, energy management, and resource allocation, as it is an essential element of supply-side management. This knowledge is obtained from accurate predictions of which traditional methods are falling short due to constantly changing demand. Hence, there is a need for Machine Learning (ML) models that are more efficient in hybridised or enhanced forms. This paper presents the use of a novel Pelican Algorithm optimised Support Vector Machine (POA-SVM), a hybridised ML Algorithm, to predict peak load and its corresponding peak hour for proper planning. It evaluates the performance of four Support Vector Machine (SVM) models: standard SVM, SVM optimised with Bayesian Optimisation (SVMB), SVM optimised with Particle Swarm Optimization (PSO-SVM) and POA-SVM on both raw and normalised datasets. Its analysis includes a comparison of performance metrics such as Root Mean Squared Error (RMSE), Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared (R²) to determine the models' accuracy and goodness of fit. Furthermore, we experiment with K-fold cross-validation and the hold-out validation method, finding that K-fold cross-validation yields better performance metrics for all models. Notably, the POA-SVM model shows superior performance across all metrics, particularly on the raw dataset, making it a robust choice for forecasting peak demand and its corresponding hour of the day. • Introducing a novel Pelican Algorithm optimised Support Vector Machine (POA-SVM). • Validating POA-SVM using performance metrics and comparison with three other models. • Validation and performance analysis of four SVM models for peak demand forecasting. • Impact of data preprocessing on SVM models; comparing raw and normalized datasets.

Topics & Concepts