Work

A hybrid K-means and KNN approach for enhanced short-term load forecasting incorporating holiday effects

Year: 2024

Type: article

Abstract: Accurate short-term load forecasting (STLF) is essential for efficient power system operations, particularly during holidays when demand patterns vary significantly. Traditional methods often struggle with these nonlinear changes, leading to increased operational costs. This research introduces a hybrid model combining K-means clustering and K-nearest neighbors (KNN) classification to enhance forecast accuracy by accounting for holiday effects. Using historical electricity consumption data, the model applies K-means to detect patterns and KNN to predict load based on the day of the week, month, and holiday status. Advanced forecasting algorithms for each cluster, including linear regression, neural networks with Bayesian optimization, Support Vector Regression with Bayesian Optimization, and long short-term memory with Bayesian optimization, were evaluated using mean absolute percentage error (MAPE), ANOVA, and Tukey's HSD test. Results show significant improvements in forecast precision, especially during holidays, with the LSTM-BO model achieving the lowest MAPE values. This hybrid approach offers a reliable tool for power system planners and operators, optimizing performance and reducing costs.

Source: Energy Reports

DOI: 10.1016/j.egyr.2024.11.050

Authors: Su Wutyi Hnin, Jessada Karnjana, Youji Kohda, Chawalit Jeenanunta

Institutions: Thammasat University, Japan Advanced Institute of Science and Technology, National Electronics and Computer Technology Center

Language: English

Topic: Energy Load and Power Forecasting

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,700