Residential Power Load Forecasting
Venue
Conference on Systems Engineering Research (CSER 2014), Elisevier B.V., Elsevier Inc/1600 John F Kennedy Boulevard Suite 1800 Philadelphia PA 19103-2879 USA (to appear)
Publication Year
2014
Authors
Jeffrey Stevenson, Patrick Day, George Ruwisch, Michael Fabian, Ryan Spencer, Rajeshbabu Thoppay, Donald Noble
BibTeX
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®.
