Temperature and Humidity Dependence for Household- and City-Wide Electricity Demand Prediction in Managua, Nicaragua

Abstract

Hourly electrical energy demand predictions improve grid reliability, stability, and minimize costs by maintaining system frequency and optimizing unit commitment and economic dispatch. Weather data, specifically ambient temperature and humidity, is commonly used as a predictor for demand. This paper utilizes the data from a recent demand response and behavioral energy efficiency pilot in Managua, Nicaragua in order to evaluate the relationship between household temperature and demand data, city-wide temperature and demand data, and the potential for utilizing household-level data to predict city-wide demand. Results from this paper indicate that temperature and humidity data can help to inform both household-level and city-wide prediction of electricity demand. Further, the available household level data was found to have a limited relationship with city-wide demand.