Incorporating Practice Theory in Sub-Profile Models for Short Term Aggregated Residential Load Forecasting

Abstract

Aspirations of grid independence could be achieved by residential power systems connected only to small highly variable loads if overall demand on the network can be accurately anticipated. Absence of the diversity found on networks with larger load cohorts or consistent industrial customers makes such overall load profiles difficult to anticipate on even a short term basis. Here, existing forecasting techniques are employed alongside enhanced classification/clustering models in proposed methods for forecasting demand in a bottom up manner. A Markov chain based sampling technique derived from practice theory of human behavior is proposed as a means of providing a forecast with low computational effort and reduced historical data requirements. The modeling approach proposed does not require seasonal adjustments or environmental data. Forecast and actual demand for a cohort of residential loads over a five-month period are used to evaluate a number of models, as well as demonstrate a significant performance improvement if utilized in an ensemble forecast.