AI

Residential Power Load Forecasting

Abstract

Abstract The prepaid electric power metering market is being driven in large part by advancements in and the adoption of Smart Grid technology. Advanced smart meters facilitate the deployment of prepaid systems with smart prepaid meters. A successful program hinges on the ability to accurately predict the amount of energy consumed on a daily basis for each end user. This method of forecasting is called Residential Power Load Forecasting (RPLF). This paper describes the systems engineering (SE) processes and tools that were used to develop a recommended load prediction model for the project sponsor, SmartGridCIS. The basic concept is that power is treated similar to a prepaid telephone in a “pay as you go” fashion. Modeling techniques explored in the analysis of alternatives (AoA) include Fuzzy Logic, Time Series Moving Average, and Artificial Neural Networks (ANN). SE tools such as prioritization and Pugh matrices were used to choose the best-fit model, which ended up being the ANN. Cognitive systems engineering was used in conjunction with the task analysis. Requirements were developed using the commercial tool IBM Rational DOORS®.