A Comparison of Nineteen Various Electricity Consumption Forecasting Approaches and Practicing to Five Different Households in Turkey

TL;DR

This study compares nineteen different electricity consumption forecasting methods applied to five Turkish households, identifying the most accurate models based on error metrics and forecasting future consumption.

Contribution

It provides a comprehensive comparison of diverse forecasting techniques on household electricity data and determines the most effective models for short-term prediction.

Findings

Best models identified by lowest MAPE, MAD, MSD

Forecasted electricity consumption for June-September 2016

Comparison of model accuracy across households

Abstract

The accuracy of the household electricity consumption forecast is vital in taking better cost effective and energy efficient decisions. In order to design accurate, proper and efficient forecasting model, characteristics of the series have to been analyzed. The source of time series data comes from Online Enerjisa System, the system of electrical energy provider in capital of Turkey, which consumers can reach their latest two year period electricity consumptions; in our study the period was May 2014 to May 2016. Various techniques had been applied in order to analyze the data; classical decomposition models; standard typed and also with the centering moving average method, regression equations, exponential smoothing models and ARIMA models. In our study, nine teen different approaches; all of these have at least diversified aspects of methodology, had been compared and the best model for forecasting were decided by considering the smallest values of MAPE, MAD and MSD. As a first step we took the time period May 2014 to May 2016 and found predicted value for June 2016 with the best forecasting model. After finding the best forecasting model and fitted value for June 2016, than validating process had been taken place; we made comparisons to see how well the real value of June 2016 and forecasted value for that specific period matched. Afterwards we made electrical consumption forecast for the following 3 months; June-September 2016 for each of five households individually.