Evaluating regional climate-electricity demand nexus: A composite Bayesian predictive framework

Climatic variations significantly influence the shape of end-use electricity demand curves. Although the climate sensitivity of end-use electricity demand is well-established, projecting medium- and long-term future demand remains a significant challenge mostly due to a multitude of uncertainties involved in the modeling process. In this paper, we leveraged a state-of-the-art Bayesian approach to develop rigorously validated regional prediction models of the climate demand nexus conditioned on the intensity level of demand. The prediction models were developed for the residential and commercial sectors for the top eight energy-intensive states in the U.S. A key contribution of this work was to illustrate the asymmetry in the sensitivity of load to climate. More specifically, our results demonstrated a greater sensitivity of the high-intensity end-use demand to climate variability as compared to the moderate-intensity end-use demand. In addition, our results helped identify mean dew point temperature as the key predictor of the climate-sensitive portion of both residential and commercial electricity demands, irrespective of the demand intensity levels. Wind speed was identified as the second most important predictor of the high-intensity (i.e., >= 3rd quartile) end-use demand, while electricity price was found to be the key predictor of the moderate-intensity (i.e., <3rd quartile) end-use demand. The influence of precipitation on the residential and commercial sectors' moderate end-use demand was found to be more variable. Precipitation was found to influence the commercial sector's electricity demand more significantly compared to the residential sector's demand.