scholarly journals A Novel Hybrid Feature Selection Method for Day-Ahead Electricity Price Forecasting

Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8455
Author(s):  
Ankit Kumar Srivastava ◽  
Ajay Shekhar Pandey ◽  
Rajvikram Madurai Elavarasan ◽  
Umashankar Subramaniam ◽  
Saad Mekhilef ◽  
...  
Keyword(s):  
Feature Selection ◽  
Electricity Markets ◽  
Classification Tree ◽  
Feature Selection Method ◽  
Regression Tree ◽  
Forecast Accuracy ◽  
Electricity Price ◽  
Price Forecasting ◽  
Electricity Price Forecasting ◽  
Optimal Feature

The paper proposes a novel hybrid feature selection (FS) method for day-ahead electricity price forecasting. The work presents a novel hybrid FS algorithm for obtaining optimal feature set to gain optimal forecast accuracy. The performance of the proposed forecaster is compared with forecasters based on classification tree and regression tree. A hybrid FS method based on the elitist genetic algorithm (GA) and a tree-based method is applied for FS. Making use of selected features, aperformance test of the forecaster was carried out to establish the usefulness of the proposed approach. By way of analyzing and forecasts for day-ahead electricity prices in the Australian electricity markets, the proposed approach is evaluated and it has been established that, with the selected feature, the proposed forecaster consistently outperforms the forecaster with a larger feature set. The proposed method is simulated in MATLAB and WEKA software.

scholarly journals Neural Network Based Model Comparison for Intraday Electricity Price Forecasting

Energies ◽  
2019 ◽  
Vol 12 (23) ◽  
pp. 4557 ◽  
Author(s):  
Ilkay Oksuz ◽  
Umut Ugurlu
Keyword(s):  
Neural Networks ◽  
Electricity Markets ◽  
Electricity Market ◽  
Network Architecture ◽  
Model Comparison ◽  
Market Price ◽  
Absolute Error ◽  
Electricity Price ◽  
Price Forecasting ◽  
Electricity Price Forecasting

The intraday electricity markets are continuous trade platforms for each hour of the day and have specific characteristics. These markets have shown an increasing number of transactions due to the requirement of close to delivery electricity trade. Recently, intraday electricity price market research has seen a rapid increase in a number of works for price prediction. However, most of these works focus on the features and descriptive statistics of the intraday electricity markets and overlook the comparison of different available models. In this paper, we compare a variety of methods including neural networks to predict intraday electricity market prices in Turkish intraday market. The recurrent neural networks methods outperform the classical methods. Furthermore, gated recurrent unit network architecture achieves the best results with a mean absolute error of 0.978 and a root mean square error of 1.302. Moreover, our results indicate that day-ahead market price of the corresponding hour is a key feature for intraday price forecasting and estimating spread values with day-ahead prices proves to be a more efficient method for prediction.

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

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6104
Author(s):  
Alireza Pourdaryaei ◽  
Mohammad Mohammadi ◽  
Mazaher Karimi ◽  
Hazlie Mokhlis ◽  
Hazlee A. Illias ◽  
...  
Keyword(s):  
Feature Selection ◽  
Search Algorithm ◽  
Power Market ◽  
Electricity Price ◽  
Price Forecasting ◽  
Feature Selection Technique ◽  
Selection Technique ◽  
Backtracking Search ◽  
Electricity Price Forecasting ◽  
Input Variables

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.

scholarly journals A Novel Feature Selection and Short-Term Price Forecasting Based on a Decision Tree (J48) Model

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3665
Author(s):  
Ankit Kumar Srivastava ◽  
Devender Singh ◽  
Ajay Shekhar Pandey ◽  
Tarun Maini
Keyword(s):  
Feature Selection ◽  
Decision Tree ◽  
Performance Test ◽  
Feature Selection Method ◽  
Forecast Accuracy ◽  
Price Forecasting ◽  
Decision Tree Classifier ◽  
Short Term ◽  
Tree Classifier ◽  
Minimum Number

A novel feature selection method based on a decision tree (J48) for price forecasting is proposed in this work. The method uses a genetic algorithm along with a decision tree classifier to obtain the minimum number of features giving an optimum forecast accuracy. The usefulness of the proposed approach is established through the performance test of the forecaster using the feature selected by this approach. It is found that the forecast with the selected feature consistently out-performed than that having larger feature set.

scholarly journals Ensemble Prediction Model with Expert Selection for Electricity Price Forecasting

2016 ◽  
Author(s):  
Bijay Neupane ◽  
Wei Lee Woon ◽  
Zeyar Aung
Keyword(s):  
Prediction Model ◽  
Electricity Markets ◽  
Electricity Price ◽  
Weather Data ◽  
Ensemble Prediction ◽  
Price Forecasting ◽  
Electricity Prices ◽  
Electricity Price Forecasting ◽  
Pattern Sequence ◽  
Weight Method

Day-ahead forecasting of electricity prices is important in deregulated electricity markets for all the stakeholders: energy wholesalers, traders, retailers, and consumers. Electricity price forecasting is an inherently difficult problem due to its special characteristic of dynamicity and non-stationarity. In this paper, we present a robust price forecasting mechanism that shows resilience towards aggregate demand response effect and provides highly accurate forecasted electricity prices to the stakeholders in a dynamic environment. We employ an ensemble prediction model in which a group of different algorithms participate in predicting the price for each hour of a day. We propose two different strategies, namely, Fixed Weight Method (FWM) and Varying Weight Method (VWM), for selecting each hour's expert algorithm from the set of participating algorithms. In addition, we utilize a carefully engineered set of features selected from a pool of features derived from information such as past electricity price data, weather data, and calendar data. The proposed ensemble model offers better results than both the Pattern Sequence-based Forecasting (PSF) method and our own previous work using Artificial Neural Networks (ANN) alone do on the datasets for New York, Australian, and Spanish electricity markets.

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