  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.
