Analyzing the Effects of COVID-19 Pandemic on the Energy Demand: the Case of Northern Italy

TL;DR

This study uses neural networks and mobility data to analyze how COVID-19 lockdowns affected energy demand in Northern Italy, revealing insights into the relationship between human mobility and power consumption during the pandemic.

Contribution

The paper introduces a neural network-based approach to estimate and analyze energy demand changes during COVID-19, integrating mobility data for comprehensive insights.

Findings

Significant reduction in power demand during lockdown periods.

Strong correlation between mobility decline and energy consumption decrease.

Insights into power system behavior during unprecedented societal disruptions.

Abstract

The COVID-19 crisis is profoundly influencing the global economic framework due to restrictive measures adopted by governments worldwide. Finding real-time data to correctly quantify this impact is very significant but not as straightforward. Nevertheless, an analysis of the power demand profiles provides insight into the overall economic trends. To accurately assess the change in energy consumption patterns, in this work we employ a multi-layer feed-forward neural network that calculates an estimation of the aggregated power demand in the north of Italy, (i.e, in one of the European areas that were most affected by the pandemics) in the absence of the COVID-19 emergency. After assessing the forecasting model reliability, we compare the estimation with the ground truth data to quantify the variation in power consumption. Moreover, we correlate this variation with the change in mobility behaviors during the lockdown period by employing the Google mobility report data. From this unexpected and unprecedented situation, we obtain some intuition regarding the power system macro-structure and its relation with the overall people's mobility.