A new framework for electricity price forecasting via multi-head self-attention and CNN-based techniques in the competitive electricity market

Due to recent technical improvements, the smart grid has become a feasible platform for electricity market participants to successfully regulate their bidding process based on demand-side management (DSM) perspectives. At this level, practical design, implementation, and assessment of numerous demand response mechanisms and robust short-term price forecasting development in day-ahead transactions are all critical. The accuracy and effectiveness of the day-ahead price forecasting process are crucial concerns in a deregulated market. In this market, the reason for low accuracy is the limitation of electricity generation compared to the electricity demand variations. Hence, this study proposes a suitable technique for forecasting electricity prices using a multi-head self-attention and Convolutional Neural networks (CNN) based approach. Further, this study develops a feature selection technique using mutual information (MI) and neural networks (NN) to choose suitable input variable subsets significantly affecting electricity price predictions simultaneously. The combination of MI and NN reduces the number of input features used in the model, thereby decreasing the computational complexity of the NN. The actual data sets from the Ontario electricity market in 2020 are acquired to verify the simulation results. Finally, the simulation results proved the efficiency of the proposed method by demonstrating increased accuracy by attaining the lowest average value for MAPE and RMSE with a value of 1.75% and 0.0085, respectively, and compared to results obtained by recent computational intelligence approaches. By attaining accurate electricity price results, the significance of this study can be summed up as aiding the electricity industry's operators in administering effective energy management, efficient resource allocation, and informed decision-making.© 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

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

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.© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Machine Learning Ensembles for Grid Congestion Price Forecasting

Title from PDF of title page, viewed June 21, 2023Thesis advisor: Reza DerakhshahniVitaIncludes bibliographical references (pages 54-56)Thesis (M.S.)--Department of Computer Science and Electrical Engineering. University of Missouri--Kansas City, 2023In this thesis, we embarked on a comprehensive study to develop a cutting-edge model for forecasting real-time electricity prices across 35 nodes within the PJM zone. The task at hand was particularly challenging, given the volatility of the day-ahead electricity market and the numerous factors that influence prices, such as load variations, weather conditions, and historical prices. Our objective was to devise a model that could provide more accurate day-ahead price forecasts than existing methods. To achieve this goal, we proposed an ensemble-based approach that leveraged the strengths of low-bias and high-variance machine learning models. To handle missing values, we employed K-Nearest Neighbors (KNN) imputation. To enhance the performance of the models, we employed Principal Component Analysis (PCA) and correlation feature selection techniques. We then employed a direct multi-output strategy to forecast real-time prices. Our ensemble incorporated a variety of models such as Support Vector Regression (SVR), Huber Regression, and deep neural networks such as Convolutional Neural Network (CNN), Long-Short Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Temporal Convolutional Network (TCN). Our results on test data from the first half of 2021 demonstrate that our proposed strategy outperforms any single model by 8.75% over all 35 nodes and beats the day-ahead prices. However, we noticed a decrease in testing accuracy in the latter half of 2021, indicating a need for a more dynamic ensemble fusion. In conclusion, our research provides valuable insights into electricity price forecasting and illustrates the effectiveness of ensemble learning techniques, incremental learning, and deep neural networks for time series forecasting. Our proposed method can be utilized by energy traders, independent system operators, and policymakers to make more informed decisions in the uncertain and volatile energy market.Introduction -- Literature review -- Data collection and preprocessing -- Data modellin

Advanced forecasting methods for renewable generation and loads in modern power systems

The PhD Thesis deals with the problem of forecasting in power systems, i.e., a wide topic that today covers many and many needs, and that is universally acknowledged to require further deep research efforts. After a brief discussion on the classification of forecasting systems and on the methods that are currently available in literature for forecasting electrical variables, stressing pros and cons of each approach, the PhD Thesis provides four contributes to the state of the art on forecasting in power systems where literature is somehow weak. The first provided contribute is a Bayesian-based probabilistic method to forecast photovoltaic (PV) power in short-term scenarios. Parameters of the predictive distributions are estimated by means of an exogenous linear regression model and through the Bayesian inference of past observations. The second provided contribute is a probabilistic competitive ensemble method once again to forecast PV power in short-term scenarios. The idea is to improve the quality of forecasts obtained through some individual probabilistic predictors, by combining them in a probabilistic competitive approach based on a linear pooling of predictive cumulative density functions. A multi-objective optimization method is proposed in order to guarantee elevate sharpness and reliability characteristics of the predictive distribution. The third contribute is aimed to the development of a deterministic industrial load forecasting method suitable in short-term scenarios, at both aggregated and single-load levels, and for both active and reactive powers. The deterministic industrial load forecasting method is based on multiple linear regression and support vector regression models, selected by means of 10-fold cross-validation or lasso analysis. The fourth contribute provides advanced PDFs for the statistical characterization of Extreme Wind Speeds (EWS). In particular, the PDFs proposed in the PhD Thesis are an Inverse Burr distribution and a mixture Inverse Burr – Inverse Weibull distribution. The mixture of an Inverse Burr and an Inverse Weibull distribution allows to increase the versatility of the tool, although increasing the number of parameters to be estimated. This complicates the parameter estimation process, since traditional techniques such as the maximum likelihood estimation suffer from convergence problems. Therefore, an expectation-maximization procedure is specifically developed for the parameter estimation. All of the contributes presented in the PhD Thesis are tested on actual data, and compared to the state-of-the-art benchmarks to assess the suitability of each proposal