Electricity requirement prediction using time series and Facebooks PROPHET

Rachel Joy Chadalavada,Srinivasaiah Raghavendra,V Rekha

doi:10.17485/ijst/v13i47.1847

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. Keywords: Electricity; forecasting; PROPHET

Highlights

Forecasting is pretty much required everywhere nowadays
The target of this work was to think about vitality request forecast esteems using the PROPHET and ARIMA techniques
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

Summary

Introduction

Forecasting is pretty much required everywhere nowadays. Energy consumption is increasing day after day, which in the future may lead to a lack of energy . As and how product sale is being forecasted, the weather is being forecasted and so on, so should energy consumption be forecasted. This energy consumption forecasting will help in knowing the consumption of electricity that helps in securing the future.

Objectives

Results

Conclusion