HOUSEHOLD ELECTRICITY LOAD FORECASTING TOWARD DEMAND RESPONSE PROGRAM USING DATA MINING TECHNIQUES IN A TRADITIONAL POWER GRID

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