Optimizing residential demand response in Ghana through iterative techniques and home appliance trend analysis

Abstract

This study examines the concept of demand response in household appliance use. Its primary aim is to explore the factors influencing electricity consumption behavior and employ K-means clustering to group households, estimating daily electricity consumption patterns. This understanding is essential for the development of effective demand response strategies within the Greater Accra Region, Ghana. The research leveraged metrics, such as the Silhouette Score and principal component analysis to ensure the quality of the clustering process, effectively combining qualitative and quantitative data. Insights were enhanced by incorporating consumer behavior surveys to better comprehend appliance use trends and optimize demand response strategies. The findings emphasize differences in voltage, intensity, power consumption, and smart meter data among different household clusters. Notably, clusters 1 and 3 emerge as high energy consumers, particularly in water and cold appliances. These insights offer valuable guidance for targeted energy management and optimization strategies. This study underscores the significance of using consumer behavior insights to enhance and optimize demand response programs, providing essential guidance to energy stakeholders, particularly in Ghana, for the efficient optimization of electricity consumption and the successful implementation of demand response initiatives.