Abstract
COVID-19 non-pharmaceutical interventions (NPIs) are changing human mobility patterns; however, the effects on power systems remain unclear. Previous loads and timings along with weather features are often used in literature as input features in load forecasting, but these may be insufficient during COVID-19. As a result, this paper proposes an analytical framework to assess the impact of COVID-19 on power system operation as well as day-ahead electricity prices in Ireland. To improve peak demand forecasting during pandemics, we incorporate mobility, NPIs, and COVID-19 cases as complementary input features and representative of human behaviour changes. By defining different combinations of these explanatory features, several Machine Learning (ML) algorithms are applied and their performance is compared with the baseline scenario currently used in the literature. Using SHapley Additive Explanations (SHAP), we interpret the best performing model, Light Gradient Boosted Machine, to determine the influence of each feature on the predicted outcomes. We discover that typical load forecasting features still influence ML outcomes the most, but mobility-related changes are also significant. Our finding shows that NPIs impact human behaviour and electricity consumption during times of crisis and can be used in the context of load forecasting to assist policymakers and energy distributors.
Highlights
A S of September 27, 2021, there have been more than 387,000 confirmed cases of COVID-19 and 5,209 deaths associated with COVID-19 in Ireland [1]
We investigate how mobility-related features can be incorporated into current load predictions in order to estimate peak demand during the pandemic as a regression problem, since estimating unforeseen events solely based on past values may not be sufficient during a health crisis [9]
These represent the complementary features that we introduced along with features such as meteorological[26] collected from Met Eireann [27], power system collected from ENTSOE transparency platform [28], time feature [29] and historical data normally used for load forecasting [12]
Summary
A S of September 27, 2021, there have been more than 387,000 confirmed cases of COVID-19 and 5,209 deaths associated with COVID-19 in Ireland [1]. In Ireland, where recently the transmission system operator, EirGrid, issued an amber warning because of a generation shortfall [6], market prices have been increasing due to generation shortfall and high CO2 spot prices and reached 419 C/MWh on September 14, 2021 It is all happening outside the winter peak demand, which raises concerns associated with having an accurate prediction of peak demand. Apart from the repercussions of the COVID-19 pandemic, the capacity margin available to meet the peak electricity demand in Ireland has been reduced over the last five years by around 71.5% owing to an increase in demand and generator forced outage rates [7] This problem could become more severe if renewable energy sources (RES) or interconnectors cannot provide the required support. We investigate how mobility-related features can be incorporated into current load predictions in order to estimate peak demand during the pandemic as a regression problem, since estimating unforeseen events solely based on past values may not be sufficient during a health crisis [9]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
