Data Analytics and Machine Learning for Reliable Energy Management: A Case Study

Renewable electric energy with reliable supply contributes to society, the economy, and the environment. Careful management of electric power from the consumers’ side is crucial on top of stable production, transmission, and distribution systems for reliable consumption. Electric power supply and consumption have been problematic in urban areas of Ethiopia, where frequent power interruptions come from overloaded transmission and distribution systems. In this paper, we proposed a focused Demand Side Management approach for improving reliable consumption in Addis Ababa. We used data analytics and machine learning (K-mean and long and short-term memory) approaches to understand the data, identify potential customers, and predict the aggregate substation load. We identified intermediate and supper-peak demand hours and potential customers for price-based demand load shifting management. Further, the analysis shows that an increase in electric prices at peak hours causes a reduction in electric demand. Consequently, it reduces distribution load and improves reliability.