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Machine Learning Ensembles For Grid Congestion Price Forecasting
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Book Synopsis Machine Learning Ensembles for Grid Congestion Price Forecasting by : Asim Javed
Download or read book Machine Learning Ensembles for Grid Congestion Price Forecasting written by Asim Javed and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In 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.
Book Synopsis Valuing Energy Storage in Electricity Grids by : Shun Him Wong
Download or read book Valuing Energy Storage in Electricity Grids written by Shun Him Wong and published by . This book was released on 2018 with total page 87 pages. Available in PDF, EPUB and Kindle. Book excerpt: Meeting climate change mitigation targets likely requires the integration of large amounts of renewable energy generation, as well as energy storage systems, into the electric grid. However, the deployment of energy storage systems will remain limited until they become economically attractive, with or without government policy. One of the most profitable and widely studied energy storage system ventures is realtime temporal arbitrage, where the decision to charge or discharge the energy storage device is made according to some charging policy or decision rules, ideally charging when electricity prices are low and discharging when prices are high. In this thesis, state-of-the-art Machine Learning methods in the field of electricity price forecasting were used to accurately predict electricity prices. An improvement on existing recurrent neural network methods was introduced, using contextual knowledge of nodal prices and information such as geolocational spatial correlation data. It was then demonstrated that these prices can be used to inform a charging policy for an energy storage device which will maximize its associated arbitrage revenue. The most profitable policy requires perfect foresight of electricity prices, and hence the true valuation of the energy storage device given imperfect forecasts is bounded from above by a valuation using perfect foresight. The effect of improvements in electricity price forecasting accuracy on the valuation of energy storage systems is then explored using simulations, which places an implicit value on the improvement of electricity price forecasting methods. The impact of these improvements on the introduction of energy storage systems into the grid is then evaluated.
Book Synopsis Electricity Price Forecasting by : Kenneth Henry Lee (Jr.)
Download or read book Electricity Price Forecasting written by Kenneth Henry Lee (Jr.) and published by . This book was released on 2018 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt: Locational marginal pricing (LMP) is a pricing mechanism used in electricity transmission systems which reflects price differentials based upon locational availability and system constraints. If a load in the system cannot meet its demand from the cheapest available generation sources, then it must draw power from more expensive sources, causing a price differential, also called congestion. Many electric transmission systems around the world have adopted this policy in order to reflect this reality and create a more transparent pricing environment. Electricity price forecasting (EPF) is used to make several important economic decisions across the grid, both for generation and load entities, including bidding, trading, and arbitrage. EPF has been studied extensively for the past twenty years, the most successful models relying on multilayer perceptrons (MLPs) or recurrent neural networks, but only focus on univariate time series. With the plethora of data available in the EPF setting, new developments in deep learning can leverage multivariate relationships and improve upon simpler models used in the past. In this report, we employ a modification of the WaveNet architecture for electricity price forecasting of the Day-Ahead-Electricity Market (DAM) in the Electricity Reliability Council of Texas (ERCOT) grid.
Book Synopsis Data Analytics in Power Markets by : Qixin Chen
Download or read book Data Analytics in Power Markets written by Qixin Chen and published by Springer Nature. This book was released on 2021-10-01 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book aims to solve some key problems in the decision and optimization procedure for power market organizers and participants in data-driven approaches. It begins with an overview of the power market data and analyzes on their characteristics and importance for market clearing. Then, the first part of the book discusses the essential problem of bus load forecasting from the perspective of market organizers. The related works include load uncertainty modeling, bus load bad data correction, and monthly load forecasting. The following part of the book answers how much information can be obtained from public data in locational marginal price (LMP)-based markets. It introduces topics such as congestion identification, componential price forecasting, quantifying the impact of forecasting error, and financial transmission right investment. The final part of the book answers how to model the complex market bidding behaviors. Specific works include pattern extraction, aggregated supply curve forecasting, market simulation, and reward function identification in bidding. These methods are especially useful for market organizers to understand the bidding behaviors of market participants and make essential policies. It will benefit and inspire researchers, graduate students, and engineers in the related fields.
Book Synopsis A Customer Agnostic Machine Learning Based Peak Electric Load Days Forecasting Methodology for Consumers with and Without Renewable Electricity Generation by : Omar Aponte
Download or read book A Customer Agnostic Machine Learning Based Peak Electric Load Days Forecasting Methodology for Consumers with and Without Renewable Electricity Generation written by Omar Aponte and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The adoption of electricity generation from renewable sources, as well as the push for a speedy electrification of sectors such as transportation and buildings, makes peak electric load management an essential aspect to ensure the electric grid’s reliability and safety. Utilities have established peak load charges that can amount to up to 70% of electricity costs to transfer the financial burden of managing these loads to the consumers. These pricing schemes have created a need for efficient peak electric load management strategies that consumers can implement in order to reduce the financial impact of this type of load. Research has shown that the impact of peak load charges can be reduced by acting on the intelligence provided by peak electric load days (PELDs) forecasts. Unfortunately, published PELDs forecasting methodologies have not addressed the increasing number of facilities adopting behind the meter renewable electricity generation. The presence of this type of intermittent generation adds substantial complexity and other challenges to the PELDs forecasting process. The work reported in this dissertation is organized in terms of its three main contributions to the body of knowledge and to society. First, the development and testing of a first of its kind PELDs forecasting methodology able to accurately predict upcoming PELDs for a consumer regardless of the presence or absence of renewable electricity generation. Experimental results showed that 93% and 90% of potential savings (approximately US$ 142,129.01 and US$ 123,100.74) could be achieved by a consumer with and a consumer without behind the meter solar generation respectively. The second contribution is the development and testing of a novel methodology that allows virtually any type of consumer to determine an efficient electricity demand threshold value before the start of a billing period. This threshold value allows consumers to proactively trigger demand response actions and reduce peak demand charges without receiving any type of signal or information from the utility. Experimental results showed 65% to 82% of total potential demand charge reductions achieved during a year for three different consumers: residential, industrial, and educational with solar generation. These results translate to US$ 149.09, US$ 23,290.00, and US$ 107,610.00 in demand charges savings a year respectively. As a third contribution, we present experimental results that show how the implementation of machine learning based ensemble classification techniques improves the PELDs forecasting methodology’s performance beyond previously published ensemble techniques for three different consumers."--Abstract.
Book Synopsis Artificial Intelligence Enabled Computational Methods for Smart Grid Forecast and Dispatch by : Yuanzheng Li
Download or read book Artificial Intelligence Enabled Computational Methods for Smart Grid Forecast and Dispatch written by Yuanzheng Li and published by Springer. This book was released on 2023-06-10 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the increasing penetration of renewable energy and distributed energy resources, smart grid is facing great challenges, which could be divided into two categories. On the one hand, the endogenous uncertainties of renewable energy and electricity load lead to great difficulties in smart grid forecast. On the other hand, massive electric devices as well as their complex constraint relationships bring about significant difficulties in smart grid dispatch. Owe to the rapid development of artificial intelligence in recent years, several artificial intelligence enabled computational methods have been successfully applied in the smart grid and achieved good performances. Therefore, this book is concerned with the research on the key issues of artificial intelligence enabled computational methods for smart grid forecast and dispatch, which consist of three main parts. (1) Introduction for smart grid forecast and dispatch, in inclusion of reviewing previous contribution of various research methods as well as their drawbacks to analyze characteristics of smart grid forecast and dispatch. (2) Artificial intelligence enabled computational methods for smart grid forecast problems, which are devoted to present the recent approaches of deep learning and machine learning as well as their successful applications in smart grid forecast. (3) Artificial intelligence enabled computational methods for smart grid dispatch problems, consisting of edge-cutting intelligent decision-making approaches, which help determine the optimal solution of smart grid dispatch. The book is useful for university researchers, engineers, and graduate students in electrical engineering and computer science who wish to learn the core principles, methods, algorithms, and applications of artificial intelligence enabled computational methods.
Book Synopsis Predictive Modelling for Energy Management and Power Systems Engineering by : Ravinesh Deo
Download or read book Predictive Modelling for Energy Management and Power Systems Engineering written by Ravinesh Deo and published by Elsevier. This book was released on 2020-09-30 with total page 553 pages. Available in PDF, EPUB and Kindle. Book excerpt: Predictive Modeling for Energy Management and Power Systems Engineering introduces readers to the cutting-edge use of big data and large computational infrastructures in energy demand estimation and power management systems. The book supports engineers and scientists who seek to become familiar with advanced optimization techniques for power systems designs, optimization techniques and algorithms for consumer power management, and potential applications of machine learning and artificial intelligence in this field. The book provides modeling theory in an easy-to-read format, verified with on-site models and case studies for specific geographic regions and complex consumer markets. Presents advanced optimization techniques to improve existing energy demand system Provides data-analytic models and their practical relevance in proven case studies Explores novel developments in machine-learning and artificial intelligence applied in energy management Provides modeling theory in an easy-to-read format
Book Synopsis A Two-stage Supervised Learning Approach for Electricity Price Forecasting by Leveraging Different Data Sources by : Shuman Luo
Download or read book A Two-stage Supervised Learning Approach for Electricity Price Forecasting by Leveraging Different Data Sources written by Shuman Luo and published by . This book was released on 2019 with total page 54 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the years, the growing penetration of renewable energy into the electricity market has resulted in a significant change in the electricity market price. This change makes the existing forecasting method prone to error, decreasing the economic benefits. Hence, more precise forecasting methods need to be developed. This paper starts with a survey and benchmark of existing machine learning approaches for forecasting the real-time market (RTM) price. While these methods provide sufficient modeling capability via supervised learning, their accuracy is still limited due to the single data source, e.g., historical price information only. In this paper, a novel two-stage supervised learning approach is proposed by diversifying the data sources such as highly correlated power data. This idea is inspired by the recent load forecasting methods that have shown extremely well performances. Specifically, the proposed two-stage method, namely the rerouted method, learns two types of mapping rules. The first one is the mapping between the historical wind power and the historical price. The second is the forecasting rule for wind generation. Based on the two rules, we forecast the price via the forecasted generation and the first learned mapping between power and price. Additionally, we observed that it is not the more training data the better, leading to our validation steps to quantify the best training intervals for different datasets. We conduct comparisons of numerical results between existing methods and the proposed methods based on datasets from the Electric Reliability Council of Texas (ERCOT). For each machine learning step, we examine different learning methods, such as polynomial regression, support vector regression, neural network, and deep neural network. The results show that the proposed method is significantly better than existing approaches when renewables are involved.
Book Synopsis Advanced Technologies for Planning and Operation of Prosumer Energy Systems, volume III by : Bin Zhou
Download or read book Advanced Technologies for Planning and Operation of Prosumer Energy Systems, volume III written by Bin Zhou and published by Frontiers Media SA. This book was released on 2024-07-30 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: Prosumers, such as energy storage, smart home, and microgrids, are the consumers who also produce and share surplus energy with other users. With capabilities of flexibly managing the generation, storage and consumption of energy in a simultaneous manner, prosumers can help improve the operation efficiency of smart grid. Due to the rapid expansion of prosumer clusters, the planning and operation issues of prosumer energy systems have been increasingly raised. Aspects including energy infrastructure design, energy management, system stability, etc., are urgently required to be addressed while taking full advantage of prosumers' capabilities. However, up to date, the research on prosumers has not drawn sufficient attention. This proposal presents the need to introduce a Research Topic on prosumer energy systems in Frontiers in Energy Research. We believe this Research Topic can promote the research on advanced planning and operation technologies of prosumer energy systems and contribute to the carbon neutrality for a sustainable society.
Book Synopsis Electricity Market Forecast Using Machine Learning Approaches by : Jian Xu (Ph. D in electrical and computer engineering)
Download or read book Electricity Market Forecast Using Machine Learning Approaches written by Jian Xu (Ph. D in electrical and computer engineering) and published by . This book was released on 2019 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: Electricity generation and load should always be balanced to maintain a tightly regulated system frequency in the power grid. Electricity generation and load both depend on many factors, such as the weather, temperature, and wind. These characteristics make the dynamics of electricity price very different from that of any other commodities or financial assets. The electricity price can exhibit hourly, daily, and seasonal fluctuations, as well as abrupt unanticipated spikes. Almost all electricity market participants use wind/load/price forecasting tools in their daily operations to optimize their operation plans, and bidding and hedging strategies, in order to maximize the profits and avoid price risks. However, the unreliable and inaccurate predictions with current forecasting tools have caused many serious problems, which can cause system instabilities and result in extreme prices even in the absence of scarcity. This dissertation presents an implementation of state of the art machine learning approaches into the forecasting tools to improve the reliability and accuracy of electricity price prediction. Most existing wholesale electricity markets consist of a Day-Ahead Market and a Real-Time Market that work together to ensure the adequacy of electricity generation capacity for the Real-Time operation to secure the reliability of the grid. The two markets have different purposes, with the Day-Ahead Market serving as preparation for and hedging against variation in the Real-Time Market. Also, the Day-Ahead Market uses hourly Day-Ahead forecasting information and the Real-Time Market uses most up-to-date Real-Time information when running calculations. So the forecasting strategies of Day-Ahead and Real-Time Markets should be different as well. The dissertation has two parts. The first part focuses on Day-Ahead price forecasting and the second part focuses on Real-Time price forecasting
Book Synopsis Machine Learning-based Renewable and Load Forecasting in Power and Energy Systems by : Cong Feng
Download or read book Machine Learning-based Renewable and Load Forecasting in Power and Energy Systems written by Cong Feng and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In the past decades, the power electricity industry has undergone tremendous evolution, which ends up with the development and establishment of electricity markets. This transformation breaks up generation services into a separate, more competitive part of the industry, and facilitates advanced techniques, such as the smart grid techniques and the integration of high-penetration levels of renewable energies, which introduce more uncertainty into the systems. To balance the electricity supply and demand at every moment, power system load and renewable energy forecasting have emerged as one of the major research fields in power and energy engineering. The development of the smart grid provides opportunities for accurate forecasting, such as the utilization of machine learning. However, the current machine learning-based forecasting techniques have several nonnegligible deficiencies, such as the over-reliance on single-algorithm machine learning models, the lack of concern of weather effects, and the neglect of heterogeneity between macroscopic superiority and local performance. This dissertation proposes to mitigate power system uncertainty by improving power system forecasting accuracy utilizing advanced machine learning techniques that are capable of providing robust, weather-aware, and widely applicable forecasting services to power system operators. Considering the unique characteristics of wind, solar, and load forecasting, this research develops advanced machine learning-based forecasting methodologies for the three forecasting tasks from different perspectives. Specifically, we first improve short-term wind forecasting accuracy by adaptively ensembling multiple machine learning models (M3) by another machine learning model, and assess the forecastability of wind sites in the United States by this enhanced M3 method. Then, short-term and very short-term solar forecasting methodologies that are aware of different weather conditions and embraces state-of-theart deep learning techniques based on sky imagery are developed. At last, we compare different aggregate strategies in short-term load forecasting and develop a reinforcement learning based dynamic model selection (QMS) methodology that is able to select the best forecasting models at every single forecasting step from a deterministic forecasting model pool or probabilistic forecasting model pool. Numerical simulations show that the developed forecasting models significantly improve forecasting accuracy, which brings benefits to various power system individuals.
Book Synopsis Web, Artificial Intelligence and Network Applications by : Leonard Barolli
Download or read book Web, Artificial Intelligence and Network Applications written by Leonard Barolli and published by Springer Nature. This book was released on 2020-03-30 with total page 1487 pages. Available in PDF, EPUB and Kindle. Book excerpt: This proceedings book presents the latest research findings, and theoretical and practical perspectives on innovative methods and development techniques related to the emerging areas of Web computing, intelligent systems and Internet computing. The Web has become an important source of information, and techniques and methodologies that extract quality information are of paramount importance for many Web and Internet applications. Data mining and knowledge discovery play a key role in many of today's major Web applications, such as e-commerce and computer security. Moreover, Web services provide a new platform for enabling service-oriented systems. The emergence of large-scale distributed computing paradigms, such as cloud computing and mobile computing systems, has opened many opportunities for collaboration services, which are at the core of any information system. Artificial intelligence (AI) is an area of computer science that builds intelligent systems and algorithms that work and react like humans. AI techniques and computational intelligence are powerful tools for learning, adaptation, reasoning and planning, and they have the potential to become enabling technologies for future intelligent networks. Research in the field of intelligent systems, robotics, neuroscience, artificial intelligence and cognitive sciences is vital for the future development and innovation of Web and Internet applications. Chapter "An Event-Driven Multi Agent System for Scalable Traffic Optimization" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Book Synopsis The Multi-Agent Transport Simulation MATSim by : Andreas Horni
Download or read book The Multi-Agent Transport Simulation MATSim written by Andreas Horni and published by Ubiquity Press. This book was released on 2016-08-10 with total page 620 pages. Available in PDF, EPUB and Kindle. Book excerpt: The MATSim (Multi-Agent Transport Simulation) software project was started around 2006 with the goal of generating traffic and congestion patterns by following individual synthetic travelers through their daily or weekly activity programme. It has since then evolved from a collection of stand-alone C++ programs to an integrated Java-based framework which is publicly hosted, open-source available, automatically regression tested. It is currently used by about 40 groups throughout the world. This book takes stock of the current status. The first part of the book gives an introduction to the most important concepts, with the intention of enabling a potential user to set up and run basic simulations. The second part of the book describes how the basic functionality can be extended, for example by adding schedule-based public transit, electric or autonomous cars, paratransit, or within-day replanning. For each extension, the text provides pointers to the additional documentation and to the code base. It is also discussed how people with appropriate Java programming skills can write their own extensions, and plug them into the MATSim core. The project has started from the basic idea that traffic is a consequence of human behavior, and thus humans and their behavior should be the starting point of all modelling, and with the intuition that when simulations with 100 million particles are possible in computational physics, then behavior-oriented simulations with 10 million travelers should be possible in travel behavior research. The initial implementations thus combined concepts from computational physics and complex adaptive systems with concepts from travel behavior research. The third part of the book looks at theoretical concepts that are able to describe important aspects of the simulation system; for example, under certain conditions the code becomes a Monte Carlo engine sampling from a discrete choice model. Another important aspect is the interpretation of the MATSim score as utility in the microeconomic sense, opening up a connection to benefit cost analysis. Finally, the book collects use cases as they have been undertaken with MATSim. All current users of MATSim were invited to submit their work, and many followed with sometimes crisp and short and sometimes longer contributions, always with pointers to additional references. We hope that the book will become an invitation to explore, to build and to extend agent-based modeling of travel behavior from the stable and well tested core of MATSim documented here.
Author :International Renewable Energy Agency IRENA Publisher :International Renewable Energy Agency (IRENA) ISBN 13 :9292601989 Total Pages :145 pages Book Rating :4.2/5 (926 download)
Book Synopsis Future of solar photovoltaic by : International Renewable Energy Agency IRENA
Download or read book Future of solar photovoltaic written by International Renewable Energy Agency IRENA and published by International Renewable Energy Agency (IRENA). This book was released on 2019-11-01 with total page 145 pages. Available in PDF, EPUB and Kindle. Book excerpt: This study presents options to fully unlock the world’s vast solar PV potential over the period until 2050. It builds on IRENA’s global roadmap to scale up renewables and meet climate goals.
Book Synopsis Intelligent Decision Technologies by : Rui Neves-Silva
Download or read book Intelligent Decision Technologies written by Rui Neves-Silva and published by Springer. This book was released on 2016-10-09 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the 57 papers accepted for presentation at the Seventh KES International Conference on Intelligent Decision Technologies (KES-IDT 2015), held in Sorrento, Italy, in June 2015. The conference consists of keynote talks, oral and poster presentations, invited sessions and workshops on the applications and theory of intelligent decision systems and related areas. The conference provides an opportunity for the presentation and discussion of interesting new research results, promoting knowledge transfer and the generation of new ideas. The book will be of interest to all those whose work involves the development and application of intelligent decision systems.
Book Synopsis Neural Information Processing by : Tom Gedeon
Download or read book Neural Information Processing written by Tom Gedeon and published by Springer. This book was released on 2019-12-06 with total page 792 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set CCIS 1142 and 1143 constitutes thoroughly refereed contributions presented at the 26th International Conference on Neural Information Processing, ICONIP 2019, held in Sydney, Australia, in December 2019. For ICONIP 2019 a total of 345 papers was carefully reviewed and selected for publication out of 645 submissions. The 168 papers included in this volume set were organized in topical sections as follows: adversarial networks and learning; convolutional neural networks; deep neural networks; embeddings and feature fusion; human centred computing; human centred computing and medicine; human centred computing for emotion; hybrid models; image processing by neural techniques; learning from incomplete data; model compression and optimization; neural network applications; neural network models; semantic and graph based approaches; social network computing; spiking neuron and related models; text computing using neural techniques; time-series and related models; and unsupervised neural models.
Book Synopsis Model-Based Machine Learning by : John Winn
Download or read book Model-Based Machine Learning written by John Winn and published by CRC Press. This book was released on 2023-11-30 with total page 469 pages. Available in PDF, EPUB and Kindle. Book excerpt: Today, machine learning is being applied to a growing variety of problems in a bewildering variety of domains. A fundamental challenge when using machine learning is connecting the abstract mathematics of a machine learning technique to a concrete, real world problem. This book tackles this challenge through model-based machine learning which focuses on understanding the assumptions encoded in a machine learning system and their corresponding impact on the behaviour of the system. The key ideas of model-based machine learning are introduced through a series of case studies involving real-world applications. Case studies play a central role because it is only in the context of applications that it makes sense to discuss modelling assumptions. Each chapter introduces one case study and works through step-by-step to solve it using a model-based approach. The aim is not just to explain machine learning methods, but also showcase how to create, debug, and evolve them to solve a problem. Features: Explores the assumptions being made by machine learning systems and the effect these assumptions have when the system is applied to concrete problems. Explains machine learning concepts as they arise in real-world case studies. Shows how to diagnose, understand and address problems with machine learning systems. Full source code available, allowing models and results to be reproduced and explored. Includes optional deep-dive sections with more mathematical details on inference algorithms for the interested reader.
