Causal Effect Estimation with Global Probabilistic Forecasting: A Case Study of the Impact of Covid-19 Lockdowns on Energy Demand
This work addresses the need for accurate uncertainty analysis in smart grid load forecasting to improve system response during policy implementations, though it is incremental as it applies existing causal estimation methods to a new domain-specific case.
The paper tackles the problem of estimating the causal impact of external interventions, such as Covid-19 lockdowns, on electricity demand by developing a framework that combines probabilistic and global forecasting models using deep learning. The result shows that during initial lockdowns in Australia and some European countries, there was a more significant decrease in troughs than peaks, with the mean remaining almost unaffected.
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.