The Effect of Climate Change on Energy Consumption Using Smart Meter Dataset

Electricity use in the urban areas is more than in the rural areas because the ratio of the population is higher in the urban areas as compared to rural areas. Energy consumption increasing day by day worldwide, so there is a need to give the best plan for the best energy resource consumption to the producers. On the other side, various other energy types are also becoming most useable in the world due to many factors like an increase in usage due to population, line losses, loss of energy due to low-quality material, and especially usage ratio increases as compared to the production of energy. This helps to save energy from misuse and to utilize the energy properly. There are various approaches applied to forecast energy consumption but, in this study, we proposed the system using LSTM, ARIMA, and Prophet model to give the solution for smart meter dataset energy consumption forecasting in a good way. After applying this approach, we conclude that the weather variables are the major factors in energy consumption such that the temperature effect is larger than other variables. The proposed system proves its performance by forecasting the dataset using these algorithms and calculate the high-grade visual graphs.