Abstract
Electric load forecasting is a challenging research problem due to the complicated nature of its dataset involving both linear and nonlinear properties. Various literatures attempted to develop forecasting models that utilized statistical in combination with machine learning approaches deal with the dataset’s linear and nonlinear components to obtain close to accurate predictions. In this paper, autoregressive integrated moving average (ARIMA) and artificial neural networks (ANN) were implemented as forecasting models for a power utility’s dataset in order to predict day-ahead electric load. Electric load data preparation, models implementation and forecasting evaluation was conducted to assess if the prediction of the models met the acceptable error tolerance for day-ahead electric load forecasting. A Java-based system made use of R Statistical Software implemented ARIMA(8,1,2) while Encog Library was used to implement the ANN model composing of Resilient Propagation as the training algorithm and Hyperbolic Tangent as the activation function. The ANN+ARIMA hybrid model was found out to deliver a Mean Absolute Percentage Error (MAPE) of 4.09% which proves to be a viable technique in electric load forecasting while showing better forecasting results than solely using ARIMA and ANN. Through this research, both statistical and machine learning approaches were implemented as a forecasting model combination to solve the linear and non-linear properties of electric load data.
Highlights
The fundamental characteristic that makes the electric power industry unique is the product: electricity
Hybrid model implementation and error measurement evaluation, this study aims to develop a day-ahead electric load forecasting model using Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN)
Only the electric load data coming from three metering points of 28,704 records from December 2013 to October 2014 were utilized since this range is best sufficient to fit an ARIMA and ANN model [1], [12]
Summary
The fundamental characteristic that makes the electric power industry unique is the product: electricity. A single megawatt, like any other commodity, is frequently bought and resold a number of times before being consumed [1][3] Load forecasting helps these power utilities make important decisions including decisions on purchasing electric power and load switching. Hybrid models are being created by combining two models and have been proven to give a more accurate and more precise measure than using the individual models [1], [4]-[6] Even these hybrid models would not always work for every electric load forecasting situation. In order to obtain accurate load prediction, power utilities would need to use a forecasting tool that would work on their data and data structure
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call