Recent Development in Electricity Price Forecasting Based on Computational Intelligence Techniques in Deregulated Power Market

Alireza Pourdaryaei,Hazlie Mokhlis,Mazaher Karimi,Hazlee A Illias,Mohammad Mohammadi,Shameem Ahmad,Seyed Hamidreza Aghay Kaboli

doi:10.3390/en14196104

Alireza Pourdaryaei, Hazlie Mokhlis + Show 5 more

Open Access

https://doi.org/10.3390/en14196104

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Abstract

The development of artificial intelligence (AI) based techniques for electricity price forecasting (EPF) provides essential information to electricity market participants and managers because of its greater handling capability of complex input and output relationships. Therefore, this research investigates and analyzes the performance of different optimization methods in the training phase of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) for the accuracy enhancement of EPF. In this work, a multi-objective optimization-based feature selection technique with the capability of eliminating non-linear and interacting features is implemented to create an efficient day-ahead price forecasting. In the beginning, the multi-objective binary backtracking search algorithm (MOBBSA)-based feature selection technique is used to examine various combinations of input variables to choose the suitable feature subsets, which minimizes, simultaneously, both the number of features and the estimation error. In the later phase, the selected features are transferred into the machine learning-based techniques to map the input variables to the output in order to forecast the electricity price. Furthermore, to increase the forecasting accuracy, a backtracking search algorithm (BSA) is applied as an efficient evolutionary search algorithm in the learning procedure of the ANFIS approach. The performance of the forecasting methods for the Queensland power market in the year 2018, which is well-known as the most competitive market in the world, is investigated and compared to show the superiority of the proposed methods over other selected methods.

Highlights

The electricity price forecasting process and development of the whole feature selection techniques were coded in MATLAB (R2019a) and run on a personal computer, with a core-2 quad processor of 2.6 GHz clock speed and 4GB RAM
Once the selected features (PoE and demand of electricity (DoE)) are finalized, the most influential features will transfer as the input variables for the forecasting process
In order to overcome the challenges encountered in the forecasting of electricity prices, various techniques have been proposed to attain a robust model with high precision

Summary

Introduction

The Queensland competitive market is considered as an electric grid that can deliver electricity in a controlled smart way from points of generation to active consumers It is done by promoting the interaction and responsiveness of the customer as well as offers a broad range of potential benefits for system operation and expansion and for market efficiency. Since the pattern of electricity demand is changing based on seasonality; short-term EPF would be more useful for real-time decision making in the deregulated electricity market for the purpose of assessing price forecasting. To execute this work, feature selection and a forecasting method are adopted to cater to short-term EPF, and data from different seasons of this market are utilized for the verification of AI application for price prediction. The last section concludes the paper and recommends the potential future development of methodologies for accurate electricity price forecasting

General Framework for the Development of Price Forecasting Method

Artificial Neural Network

Selection of Forecast Model Inputs via Multi-Objectives

Simulation Results and Discussion

Methods

Conclusions and Recommendations