ARIMA Forecasting for commercial Electricity Bills for Small scale Industry in Pre -COVID and COVID scenario in India

Abstract

ElectricalEnergy is one of the major forms of energy in the world today. All developed and developing nations majorly use electrical energy to undertake their day to day industrial activities. The consumption pattern of electrical energy differs in various small scale industries as per seasonal demand of manufacturing of products. However, COVID pandemic has changed the consumption pattern of electrical energy due to lock-down, labour shortage and other restrictions in India. The forecasting of electricity or energy used in small-scale industries is important to achieve energy conservation by improving their energy performance and also helps in reducing environmental hazards. The current study aims, to understand the temporary impact of COVID-19 pandemic on electronic kits producing small-scale industry, which is related to educational institutions of Central India. High energy consumption is directly correlated to high production capacity of any particular industry in the peak demand season. ARIMA forecasting method can be used to get the best forecasted model as per different data types. This forecasting could be helpful to understand the future trend of demand for electronic kits producing small-scale industry and to manage the resources in the best possible manner. Results and analysis reveal that in normal time, around the year there are 2 cyclic hikes in demand, followed by two cyclic dips in Bills. First hike in the demand of electronic kits is observed in the months of May-July every year due to the start of new academic sessions. After July, the first cycle of dip in orders was observed from August to September. Then the second cycle of high demand starts after October which continues till December due to the commencement of semester exams where electronic kits are required by all the technical institutions for conducting the practical experiments. Data was forecasted at 95% confidence interval; hence it can be considered a highly reliable prediction.