The Game Changer: Analytical Methods for Energy Demand Prediction Under Climate Change

Accurate prediction of electricity demand is a critical step in balancing the grid. Many factors influence electricity demand. Among these factors, climate variability has been the most pressing one in recent times, challenging the resilient operation of the grid, especially during climatic extremes. In this dissertation, fundamental challenges related to accurate characterization of the climate-energy nexus are presented in Chapters 2–4, as described below. Chapter 2 explores the cost of neglecting the role of humidity in predicting summer-time residential electricity consumption. Analysis of electricity demand in the CONUS region demonstrates that even though surface temperature—the most widely used metric for characterising heat stress—is an important factor, it is not sufficient for accurately characterizing cooling demand. The chapter proceeds to show significant underestimations of the climate sensitivity of demand, both in the observational space as well as under climate change. Specifically, the analysis reveals underestimations as high as 10-15% across CONUS, especially in high energy consuming states such as California and Texas. Chapter 3 takes a critical look at one of the most widely used metrics, namely, the Cooling Degree Days (CDD), often calculated with an arbitrary set point temperature of 65°F or 18.3°C, ignoring possible variations due to different patterns of electricity consumption across different regions and climate zones. In this chapter, updated values are derived based on historical electricity consumption data across the country at the state level. Chapter 3 analysis demonstrates significant variation, as high as ±25%, between derived set point variables and the conventional value of 65°F. Moreover, the CDD calculation is extended to account for the role of humidity, in the light of lessons learnt in the previous chapter. Our results reveal that under climate change scenarios, the air-temperature based CDD underestimates thermal comfort by as much as ∼ 22%. The predictive analytics conducted in Chapter 2 and Chapter 3 revealed a significant challenge in characterizing the climate-demand nexuses: the ability to capture the variability at the upper tails. Chapter 4 explores this specific challenge, with the specific goal of developing an algorithm to increase prediction accuracy at the higher quantiles of the demand distributions. Specifically, Chapter 4 presents a data-centric approach at the utility level (as opposed to the state-level analyses in the previous chapters), focusing on highenergy consuming states of California and Texas. The developed algorithm shows a general improvement of 7% in the mean prediction accuracy and an improvement of 15% for the 90th quantile predictions