Electricity requirement prediction using time series and Facebooks PROPHET

Abstract

Objectives: The main objective of this research work is to forecast the electricity requirement of a particular household or an office or any building. Methods: Forecasting is done using the PROPHET model which gives better results compared to other models like ARIMA and so on. Dataset considered here is a publicly available dataset called 'Appliance' dataset with what are all the appliances that are there in the particular household and number of appliances that are running on a day at every 10 minutes interval and so on. From the entire dataset, only two attributes are selected, and Log transformation is applied to the selected attributes. Finally, the PROPHET model is applied and the forecasting is done. Findings: The findings of the proposed models are: (i) Forecasting is done for the next 30 days based on different components like daily component, weekly component and trend component (ii) Wednesday is the lowest power utilization day and, power utilization increases till Saturday and Saturday is the highest power utilization day (iii) PROPHET model makes predictions very accurate based on the future data and is easy to make predictions compared to ARIMA. Novelty: Models were trained on the dataset from January 11, 2016, to May 27, 2016 interval which is at 10 min for about 4.5 months. The models anticipated qualities that could be viewed as effective in the 30-day forecast. The accomplishment of the model in the time series expectation was investigated and analyzed.