English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The electricity industry is heavily implementing smart grid technologies toimprove reliability, availability, security, and efficiency. Thisimplementation needs technological advancements, the development of standardsand regulations, as well as testing and planning. Smart grid load forecastingand management are critical for reducing demand volatility and improving themarket mechanism that connects generators, distributors, and retailers. Duringpolicy implementations or external interventions, it is necessary to analysethe uncertainty of their impact on the electricity demand to enable a moreaccurate response of the system to fluctuating demand. This paper analyses theuncertainties of external intervention impacts on electricity demand. Itimplements a framework that combines probabilistic and global forecastingmodels using a deep learning approach to estimate the causal impactdistribution of an intervention. The causal effect is assessed by predictingthe counterfactual distribution outcome for the affected instances and thencontrasting it to the real outcomes. We consider the impact of Covid-19lockdowns on energy usage as a case study to evaluate the non-uniform effect ofthis intervention on the electricity demand distribution. We could show thatduring the initial lockdowns in Australia and some European countries, therewas often a more significant decrease in the troughs than in the peaks, whilethe mean remained almost unaffected.

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