Household Electricity Load Forecasting Toward Demand Response Program Using Data Mining Techniques in A Traditional Power Grid


Abstract views: 323 / PDF downloads: 382

Authors

Abstract

At present, the continuous increase of household electricity demand is strategic and crucial in electricity demand management. Household electricity consumers can play an important role in this issue. The rationalization of electricity consumption might be achieved by using an efficient Demand Response (DR) program. In this paper a new methodology is suggested using a combination of data mining techniques namely K-means clustering, K-Nearest Neighbors (K-NN) classification and ARIMA for electricity load forecasting using consumers' electricity prepaid bills data set of an ordinary electricity grid with prepaid electricity meters. As a result of applying this methodology, various DR programs are recommended as an attempt to assist the management of electricity system to manage the electricity demand issues from demand-side in an efficient and effective manner, which can be put into practice. A case study has been carried out in Tulkarm District, Palestine. The performance of applying the suggested methodology is measured, and the results are considered very well.Keywords: Demand Response (DR); K-means Clustering; K-Nearest Neighbor classification (K-NN); ARIMA model; Prepaid electricity metersJEL Classifications: Q4, Q41, Q47, Q49DOI: https://doi.org/10.32479/ijeep.11192

Downloads

Download data is not yet available.

Author Biography

Maher AbuBaker, An-Najah, National University, Nablus, Palestine

Lecturer at MIS Department, Faculty of Eng. & IT, An-Najah N. University.

Downloads

Published

2021-06-08

How to Cite

AbuBaker, M. (2021). Household Electricity Load Forecasting Toward Demand Response Program Using Data Mining Techniques in A Traditional Power Grid. International Journal of Energy Economics and Policy, 11(4), 132–148. Retrieved from https://www.econjournals.com/index.php/ijeep/article/view/11192

Issue

Section

Articles