Causal Effect Estimation with Global Probabilistic Forecasting: A Case Study of the Impact of Covid-19 Lockdowns on Energy Demand

TL;DR

This paper introduces a deep learning framework that combines probabilistic and global forecasting to estimate the causal impact of external interventions, like Covid-19 lockdowns, on electricity demand distributions, accounting for uncertainty.

Contribution

It presents a novel approach integrating probabilistic and global models for causal effect estimation in energy demand forecasting during external interventions.

Findings

Lockdowns caused larger decreases in demand troughs than peaks.

The mean energy demand remained largely unaffected during lockdowns.

The framework effectively captures the distributional impact of interventions.

Abstract

The electricity industry is heavily implementing smart grid technologies to improve reliability, availability, security, and efficiency. This implementation needs technological advancements, the development of standards and regulations, as well as testing and planning. Smart grid load forecasting and management are critical for reducing demand volatility and improving the market mechanism that connects generators, distributors, and retailers. During policy implementations or external interventions, it is necessary to analyse the uncertainty of their impact on the electricity demand to enable a more accurate response of the system to fluctuating demand. This paper analyses the uncertainties of external intervention impacts on electricity demand. It implements a framework that combines probabilistic and global forecasting models using a deep learning approach to estimate the causal impact distribution of an intervention. The causal effect is assessed by predicting the counterfactual distribution outcome for the affected instances and then contrasting it to the real outcomes. We consider the impact of Covid-19 lockdowns on energy usage as a case study to evaluate the non-uniform effect of this intervention on the electricity demand distribution. We could show that during the initial lockdowns in Australia and some European countries, there was often a more significant decrease in the troughs than in the peaks, while the mean remained almost unaffected.