Energy in Russia

The data is to evaluate the impact of restrictive measures introduced in connection with COVID-19 on consumption and, accordingly, on electricity generation in Russian cities, which were most susceptible to outbreaks of the coronavirus infection (Moscow, St. Petersburg, Yekaterinburg and Nizhny Novgorod). Currently, based on available data, the mobility of the population has decreased resulting in lower demand for electricity during self-isolation. Therefore, the study will be based on the hypothesis that similar changes in human behavior can be expected in the future with further spread of COVID-19 and/or the introduction of additional quarantine measures in major cities around the world. The analysis also yielded additional results: the strongest reductions in energy generation occurred in cities with high building density (7% in Moscow, 14% in Yekaterinburg). Furthermore, the decrease in energy generation in cities with low building density was not so dramatic (1% in St. Petersburg, 0% - Nizhny Novgorod). The study uses two models created with Keras LSTM. The first model forecasts power generation and uses 76 parameters. The second LSTM model forecasts new COVID-19 cases across countries, in which 10 parameters are involved.

本研究旨在评估与COVID-19疫情相关的限制措施对消费及由此产生的俄罗斯城市电力生成的影响，尤其是那些最容易受到冠状病毒感染爆发影响的城市（如莫斯科、圣彼得堡、叶卡捷琳堡和下诺夫哥罗德）。根据现有数据，人群流动性已有所降低，导致自我隔离期间的电力需求下降。因此，本研究基于以下假设：随着COVID-19的进一步传播以及全球各大城市实施额外的隔离措施，未来人类行为可能发生类似的转变。此外，分析还得出了一些额外结果：在建筑密度较高的城市中，能源生成量降幅最大（莫斯科7%，叶卡捷琳堡14%）。而在建筑密度较低的城市中，能源生成量的下降并不显著（圣彼得堡1%，下诺夫哥罗德0%）。研究采用了两种使用Keras LSTM创建的模型：第一个模型用于预测电力生成，包含76个参数；第二个LSTM模型用于预测各国的新冠病例数，涉及10个参数。