The use of computational intelligence techniques for mid-term electricity price forecasting

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementWe currently live in a world ruled by large amounts of data. Organizations’ success is highly determined by the way they foresee and assess changes occurring in the future. Predictive data analytics is the art of building and using models that create forecasts based on patterns extracted from historical data. So, it is a process of making projections about a specific event which the outcome is still unknown in the present. One of the main applications is price prediction (Kelleher, Namee, & D’Arcy, 2015). Price prediction can be applied in innumerous types of business, including the energy sector. Additionally, Big Data has created opportunities for development of new energy services and bears a promise of better energy management and conservation (Grolinger, L’Heureux, Capretz, & Seewald, 2016). Whenever prediction deals with time-series data, it can be designated as forecasting. The electricity spot prices (ESP) represent the result of the market bidding prices outcome, in the electric wholesale market. Predicting these prices is an important and impactful task for market participants, like producers, consumers and retailers, since the principal objective for such players is to achieve the lowest cost in comparison with competitors. ESP play a huge role in energy market’s decision making. It is important both for developing proper bidding strategies as well as for making conscient and sustainable investment decisions (Keynia & Heydari, 2019). Additionally, it impacts the decision of the technologies to use, for example, choosing between renewable energy generators or classic gas turbines. Furthermore, the topic of electricity prices forecasting is extremely relevant for both developed and developing countries. Developed countries search for their economic prospect’s improvement. Electric energy efficiency is a crucial metric for that improvement. Electric energy efficiency can decrease the electricity prices thanks to the reduction of consumption, thus decreasing the need of having new expensive power generation and diminishing the pressure on energy resources. Therefore, ESP behavior is an important factor in their economy. Regarding developing economies, which have faced problems to take the populations out of poverty, the electricity sector restructuring has been fundamental for helping increase the levels of economic development (Ebrahimian, Barmayoon, Mohammadi, & Ghadimi, 2018)

Novel hybridized computational paradigms integrated with five stand-alone algorithms for clinical prediction of HCV status among patients: A data-driven technique

The emergence of health informatics opens new opportunities and doors for different disease diagnoses. The current work proposed the implementation of five different stand-alone techniques coupled with four different novel hybridized paradigms for the clinical prediction of hepatitis C status among patients, using both sociodemographic and clinical input variables. Both the visualized and quantitative performances of the stand-alone algorithms present the capability of the Gaussian process regression (GPR), Generalized neural network (GRNN), and Interactive linear regression (ILR) over the Support Vector Regression (SVR) and Adaptive neuro-fuzzy inference system (ANFIS) models. Hence, due to the lower performance of the stand-alone algorithms at a certain point, four different novel hybrid data intelligent algorithms were proposed, including: interactive linear regression-Gaussian process regression (ILR-GPR), interactive linear regression-generalized neural network (ILR-GRNN), interactive linear regression-Support Vector Regression (ILR-SVR), and interactive linear regression-adaptive neuro-fuzzy inference system (ILR-ANFIS), to boost the prediction accuracy of the stand-alone techniques in the clinical prediction of hepatitis C among patients. Based on the quantitative prediction skills presented by the novel hybridized paradigms, the proposed techniques were able to enhance the performance efficiency of the single paradigms up to 44% and 45% in the calibration and validation phases, respectively.Operational Research Centre in Healthcare, Near East University, North Cyprus, Mersin-10, Turkiy

Forecasting Natural Gas Prices in the United States Using Artificial Neural Networks

Prediction of the natural gas price is imperative to producers, suppliers, traders, market makers, and bankers involved in the natural gas exploration, production transportation, and trading. Additionally, consumers are also highly affected by the changes in the price of oil and gas products. Several attempts have been made to model the energy commodity prices over the past few decades. Stochastic differential equation, linear and nonlinear regression, auto regression, and neural networks are the main techniques that have been implemented. In this thesis, three different categories of models are examined which are, stochastic differential equations, ARIMA, and autoregressive neural networks. The results indicate that, the NAR neural network provides a better fit to the given data as compared to the other proposed models. The three-layer NAR model with 6 hidden neurons was found to have the best performance in terms of one month ahead price prediction. The accuracy of the NARX model with 6 neurons was found to be higher than that of the other models. Although, this model provides a reasonable fit to the given data, it fails to capture the price spikes effectively. The sensitivity analysis shows that CDD/HDD temperatures, extreme minimum temperature, and WTI oil prices have an insignificant effect on the results. On the other hand, total consumption, total production, and mean temperature of weather impact the results significantly

Framework for collaborative intelligence in forecasting day-ahead electricity price

Electricity price forecasting in wholesale markets is an essential asset for deciding bidding strategies and operational schedules. The decision making process is limited if no understanding is given on how and why such electricity price points have been forecast. The present article proposes a novel framework that promotes human–machine collaboration in forecasting day-ahead electricity price in wholesale markets. The framework is based on a new model architecture that uses a plethora of statistical and machine learning models, a wide range of exogenous features, a combination of several time series decomposition methods and a collection of time series characteristics based on signal processing and time series analysis methods. The model architecture is supported by open-source automated machine learning platforms that provide a baseline reference used for comparison purposes. The objective of the framework is not only to provide forecasts, but to promote a human-in-the-loop approach by providing a data story based on a collection of model-agnostic methods aimed at interpreting the mechanisms and behavior of the new model architecture and its predictions. The framework has been applied to the Spanish wholesale market. The forecasting results show good accuracy on mean absolute error (1.859, 95% HDI [0.575, 3.924] EUR (MWh)−1) and mean absolute scaled error (0.378, 95% HDI [0.091, 0.934]). Moreover, the framework demonstrates its human-centric capabilities by providing graphical and numeric explanations that augments understanding on the model and its electricity price point forecasts

A review on Day-Ahead Solar Energy Prediction

Accurate day-ahead prediction of solar energy plays a vital role in the planning of supply and demand in a power grid system. The previous study shows predictions based on weather forecasts composed of numerical text data. They can reflect temporal factors therefore the data versus the result might not always give the most accurate and precise results. That is why incorporating different methods and techniques which enhance accuracy is an important topic. An in-depth review of current deep learning-based forecasting models for renewable energy is provided in this paper

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

Applications of machine learning to water resources management: A review of present status and future opportunities

Data availability: No data was used for the research described in the article.The corrected proof will be replaced by version of record in due course.Copyright © 2024 The Authors. Water is the most valuable natural resource on earth that plays a critical role in the socio-economic development of humans worldwide. Water is used for various purposes, including, but not limited to, drinking, recreation, irrigation, and hydropower production. The expected population growth at a global scale, coupled with the predicted climate change-induced impacts, warrants the need for proactive and effective management of water resources. Over the recent decades, machine learning tools have been widely applied to various water resources management-related fields and have often shown promising results. Despite the publication of several review articles on machine learning applications in water-related fields, this review paper presents for the first time a comprehensive review of machine learning techniques applied to water resources management, focusing on the most recent achievements. The study examines the potential for advanced machine learning techniques to improve decision support systems in the various sectors within the realm of water resources management, which includes groundwater management, streamflow forecasting, water distribution systems, water quality and wastewater treatment, water demand and consumption, hydropower and marine energy, water drainage systems, and flood management and defence. This study provides an overview of the state-of-the-art machine learning approaches to the water industry and how they can be used to ensure water supply sustainability, quality, and flood and drought mitigation. This review covers the most recent related studies to provide the most recent snapshot of machine learning applications in the water industry. Overall, LSTM networks have been proven to exhibit reliable performance, often outperforming ANN models, traditional machine learning models, and established physics-based models. Hybrid ML techniques have exhibited great forecasting accuracy across all water-related fields, often showing superior computational power over traditional ANNs architectures. In addition to purely data-driven models, physical-based hybrid models have also been developed to improve prediction performance. These efforts further demonstrate that Machine learning can be a powerful practical tool for water resources management. It provides insights, predictions, and optimisation capabilities to help enhance sustainable water use and management and improve socio-economic development, healthy ecosystems and human existence.EPSRC project reference 2339403 to S. Sayed and A. Ahmed