Time Series Prediction of Electricity Demand Using Adaptive Neuro-Fuzzy Inference Systems

Amevi Acakpovi,Alfred Tettey Ternor,Abdul-Shakud Iddrisu,Patrick Adjei,Nana Yaw Asabere

doi:10.1155/2020/4181045

Abstract

This paper is concerned with the reliable prediction of electricity demands using the Adaptive Neuro-Fuzzy Inference System (ANFIS). The need for electricity demand prediction is fundamental and vital for power resource planning and monitoring. A dataset of electricity demands covering the period of 2003 to 2018 was collected from the Electricity Distribution Company of Ghana, covering three urban areas namely Mallam, Achimota, and Ga East, all in Ghana. The dataset was divided into two parts: one part covering a period of 0 to 500 hours was used for training of the ANFIS algorithm while the second part was used for validation. Three scenarios were considered for the simulation exercise that was done with the MATLAB software. Scenario one considered four inputs sampled data, scenario two considered an additional input making it 5, and scenario 3 was similar to scenario 1 with the exception of the number of membership functions that increased from 2 to 3. The performance of the ANFIS algorithm was assessed by comparing its predictions with other three forecast models namely Support Vector Regression (SVR), Least Square Support Vector Machine (LS-SVM), and Auto-Regressive Integrated Moving Average (ARIMA). Findings revealed that the ANFIS algorithm can perform the prediction accurately, the ANFIS algorithm converges faster with an increase in the data used for training, and increasing the membership function resulted in overfitting of data which adversely affected the RMSE values. Comparison of the ANFIS results to other previously used methods of predicting electricity demands including SVR, LS-SVM, and ARIMA revealed that there is merit to the potentials of the ANFIS algorithm for improved predictive accuracy while relying on a quality data for training and reliable setting of tuning parameters.

Highlights

Forecasting electricity demand is vital for power generation and planning
Is paper is concerned with a medium-term load forecasting Electricity demand for a selected town in the Greater Accra Region, Mallam town, which is densely populated. is study is useful in the generation capacity planning for future network upgrades due to the increasing load demand of the community. e rest of the paper is structured to cover the methodology, the result, discussion, and conclusion sections, respectively
Electricity load forecasting is an activity that has been carried regularly in the past with numerous methods. e accuracy and reliability of this prediction varies based on the methodology used; some of the prominent methods used in the past to conduct time series electricity forecasting include the following: simple moving average (SMA), weighted moving average (WMA), simple exponential smoothing (SES), Holt linear trend (HL), Holt-Winters (HW), Auto-Regressive Integrated Moving Average (ARIMA) models, vector autoregressive (VAR) forecasting models, and artificial neural networks and support vector machines (SVM). is section presents the strengths and weaknesses of the above-mentioned methods and makes a case for the consideration of the Adaptive Neuro-Fuzzy Inference System (ANFIS) in predicting electricity load forecast

Summary

Introduction

Forecasting electricity demand is vital for power generation and planning. Accurate forecast of electricity demands presents a better understanding of the electricity network expansion and generation to sustainably cater for future demands [1,2,3]. Short-term load forecasting helps power system operators with various decisionmaking in the power system, including supply planning, generation reserve, system security, dispatching scheduling, demand-side management, and financial planning. Medium-Term Load Forecasting (MTLF) is a category of electric load forecasting that covers a time span of up to one year. It suits outages and maintenance planning, as well as load switching operation. Long-Term Load Forecasting (LTLF) is load forecasting that usually covers forecasting horizons of one to ten years and sometimes extends to several decades [9] It provides weekly/monthly forecasts for peak and valley loads which are important to expand generation, transmission, and distribution systems. Is paper is concerned with a medium-term load forecasting Electricity demand for a selected town in the Greater Accra Region, Mallam town, which is densely populated. is study is useful in the generation capacity planning for future network upgrades due to the increasing load demand of the community. e rest of the paper is structured to cover the methodology, the result, discussion, and conclusion sections, respectively

Literature Review

Results and Interpretations

Discussion