Alavi Abdullah, Sadid Md Shahriar, Ahmed Moshiur, Abid Fahim
Department of Electrical and Electronic Engineering (EEE), Islamic University of Technology, Board Bazar, Gazipur 1704, Bangladesh.
Heliyon. 2022 Jan;8(1):e08737. doi: 10.1016/j.heliyon.2022.e08737. Epub 2022 Jan 10.
The COVID-19 pandemic has disrupted our way of life and has brought challenges on a scale never seen before. Lockdown and various other social distancing rules have been implemented in order to slow down new infections. This has led to a drastic change in people's lifestyle and has altered the pattern of electricity consumption. In this paper, the effect of lockdown on the overall electricity consumption of Bangladesh is analysed. Also, a neural network-based prediction model is developed to predict the electricity consumption during a lockdown or estimate the future consumption given a lockdown is announced in the future. Furthermore, in order to compare the change in electricity consumption for commercial, residential, and industrial areas, power measurements for the lockdown and the post lockdown period are analysed in detail. Results show a significant decline in the overall electricity consumption of Bangladesh during the period of lockdown. In addition, the commercial and industrial areas showed a considerable reduction in electricity consumption. However, the changes in electricity consumption in residential areas are found negligible. This study will aid in to provide a clearer understanding on the electricity consumption pattern in the case of further lockdowns or in the event of another outbreak in the future.
新冠疫情扰乱了我们的生活方式，带来了前所未有的挑战。为减缓新感染病例，实施了封锁及各种其他社交距离规则。这导致人们的生活方式发生了巨大变化，也改变了用电模式。本文分析了封锁对孟加拉国总体用电量的影响。此外，还开发了一种基于神经网络的预测模型，用于预测封锁期间的用电量，或在未来宣布封锁时估计未来的用电量。此外，为比较商业、住宅和工业区的用电量变化，详细分析了封锁期间和封锁后时期的电力测量数据。结果显示，封锁期间孟加拉国的总体用电量显著下降。此外，商业和工业区的用电量大幅减少。然而，发现居民区的用电量变化可忽略不计。这项研究将有助于更清楚地了解在未来进一步封锁或再次爆发疫情情况下的用电模式。
