Download or read book Improving Hour ahead Hybrid Solar Irradiance Prediction Using Deep Learning and Sky Images written by Benjamin Manning and published by . This book was released on 2018 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: This research was performed to increase the optimization of hour-ahead hybrid solar irradiance prediction methods through the design and implementation of a new hybrid system and model that utilized sky images as a replacement variable for cloud types identified by overhead satellites. Improving current solar radiation prediction methods will benefit electricity producers that need to better understand the availability of solar irradiance and how it may impact their forecasting. Phase one outlines a comparison between current supervised learning methods and deep learning methods. Recurrent Neural Networks produced lower RMSE and higher R2 values and outperformed supervised learning methods. Phase two outlines building and validating a new one-hour ahead hybrid prediction model by combining a deep learning approach with a replacement feature derived from real-time image collection and location specific numerical weather features. This replacement feature was the percentage of sky cover from an observation point on the ground and was called Sky Types. Sky Types are less expensive to obtain and can be collected at any location, which also makes the prediction of solar irradiance specific to the same location. Deep learning models validated that the use of Sky Types was not only a valid substitution for cloud types, but were more optimal for training as model performance improved with reduced network topology and while still being optimal for hour-ahead predictions. To use Sky Types in the new hybrid prediction model, a system was designed to collect the sky condition information from the National Weather Service and relevant images representing the sky condition; both were captured at the same time intervals. Phase three outlines the creation of a system used for collecting images and weather data, preparing images for use by the new hybrid prediction model and building a classification model using a Convolutional Neural Network. The system's GHI predictions were validated using hour-ahead ground truth solar irradiance amounts from ten locations and averaged an RMSE of 41.26 W/m2 and outperformed GFS forecasted GHI by 32% on highly variable weather days. This new hybrid system can be used anywhere numerical weather data and sky images can be captured.
Download or read book Advances in Electrical Systems and Innovative Renewable Energy Techniques written by Mohamed Bendaoud and published by Springer Nature. This book was released on with total page 227 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Solar Irradiance Forecast from All sky Images Using Machine Learning written by Cristian Crisosto and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Solar Irradiance Forecasting Using Hybrid Ensemble Machine Learning Technique written by Josalin Jemima J and published by Mohammed Abdul Sattar. This book was released on 2024-01-02 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Economic development is impacted significantly by conventional energy sources, which are hazardous to humans and the environment. To meet the energy demand and reduce greenhouse gas emissions, the world is shifting towards alternate renewable energy sources. Photovoltaics (PV) is the most common distributed energy source for microgrid formation and one of the world's top renewable energy sources because of their modular design, minimal operational noise, and ease of maintenance. Solar photovoltaic systems, which are photovoltaic panels that turn sunlight into electricity, are one of the most common renewable energy sources. PV production is strongly dependent on solar irradiation, temperature, and other weather conditions. Predicting solar irradiance implies predicting solar power generation one or more steps ahead of time. Prediction increases photovoltaic system development and operation while providing numerous economic benefits to energy suppliers. There are numerous applications that employ prediction to improve power grid operation and planning, with the appropriate time-resolution of the forecast. Stability and regulation necessitate knowledge of solar irradiation over the following few seconds. Reserve management and load following require knowledge of solar irradiation for the next several minutes or hours. To function properly, scheduling and unit commitment requires knowledge about the next few days of solar irradiation. It is crucial to precisely measure solar irradiation since the major issue with solar energy is that it fluctuates because of its variability. Grid operators can control the demand and supply of power and construct the best solar PV plant with the help of accurate and reliable solar irradiance predictions. Electric utilities must generate enough energy to balance supply and demand. The electric sector has consequently focused on Solar PV forecasting to assist its management system, which is crucial for the growth of additional power generation, such as microgrids. Forecasting solar irradiance has always been important to renewable energy generation since solar energy generation is location and time-specific. When the estimated solar generation is available, the grid will function more consistently in unpredictable situations since solar energy generates some quantity of power every day of the year, even on cloudy days.
Download or read book Solar Irradiance Forecasting Using Neural Networks written by Alberto Eduardo Gabás Royo and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Accurate solar irradiance forecasting is essential for minimizing operational costs of solar photovoltaic (PV) generation as it is commonly used to predict the power output. This thesis presents and compares three different machine learning approaches of solar irradiance forecasting: Random Forest (RF), Feedforward Neural Networks (FNNs) and Long Short-Term Memory (LSTM) networks. Each model was tested on two different forecasts: the next hour average and the hourly day-ahead averages. The machine learning algorithms were trained and tested on data from a weather station located at Tampere University (TAU) in Tampere, Finland. Data were preprocessed before training the algorithms and the relevant features were selected. Moreover, Grid Search and Random Search techniques were used along with multiple train and validation splits to find the optimal hyperparameters for each machine learning algorithm. Persistence model is set as a baseline model for comparison while RMSE and MAE are used to quantify the prediction error. For the next hour forecast, LSTM achieved the highest accuracy in terms of RMSE (76.14 W/m2 ), 2.1% and 1.1% better than RF and FNN respectively. Instead, FNN generally produced the best results in the day-ahead forecast. In all models, the prediction error increases as the forecast horizon increases until it stabilizes at 10 hours approximately. Further, the error keeps increasing but slower. Besides, the next hour forecast models were able to predict considerably better the next hour solar irradiance than the day-ahead forecast models.
Download or read book Sky image Based Intra hour Solar Forecasting Using Independent Cloud motion Detection and Ray tracing Techniques for Cloud Shadow and Irradiance Estimation written by Jaro Nummikoski and published by . This book was released on 2013 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: Solar forecasting solutions provide utility companies with predictions of power output from large-scale solar installations or from distributed solar generation with a time scale ranging from the next few minutes up to several days ahead. These predictions decrease the risk associated with bidding renewable electricity to the regional grid. Increasing solar photovoltaic efficiency and decreasing manufacturing costs have driven solar electricity generation to become the fastest growing form of renewable electricity production. Adding solar generation in large quantities to the aging electricity grids of the world poses a problem due to the variability and intermittency of solar irradiance. The current state-of-the-art in solar forecasting is focused on the hour-ahead and day-ahead time horizons using publicly available satellite imagery or numerical weather prediction models. Conventional intra-hour forecasting methods are based on sky imagery and basic image processing and computer vision techniques. This thesis discusses the architecture of an intra-hour forecasting tool and outlines the steps involved in taking a sky image and outputting a value of irradiance at specified intra-hour intervals. The thesis includes technical discussions on obstruction masking, geometric transformation, cloud-motion detection and ray tracing for irradiance estimation. The goal is to improve and enhance conventional techniques with innovative approaches to intra-hour solar forecasting. The forecasting tool provides predictions of irradiance and the associated uncertainty through the use of a novel irradiance estimation algorithm and a Monte Carlo simulation. The ray tracing procedure allows for multiple irradiance estimations to be made at spatially distributed points, providing a high-fidelity irradiance mapping of the area within the range of the sky imager. This map can be used to accurately estimate power output from large scale solar power plants or distributed solar generation sites.
Download or read book Machine Learning and Computer Vision for Renewable Energy written by Acharjya, Pinaki Pratim and published by IGI Global. This book was released on 2024-05-01 with total page 351 pages. Available in PDF, EPUB and Kindle. Book excerpt: As the world grapples with the urgent need for sustainable energy solutions, the limitations of traditional approaches to renewable energy forecasting become increasingly evident. The demand for more accurate predictions in net load forecasting, line loss predictions, and the seamless integration of hybrid solar and battery storage systems is more critical than ever. In response to this challenge, advanced Artificial Intelligence (AI) techniques are emerging as a solution, promising to revolutionize the renewable energy landscape. Machine Learning and Computer Vision for Renewable Energy presents a deep exploration of AI modeling, analysis, performance prediction, and control approaches dedicated to overcoming the pressing issues in renewable energy systems. Transitioning from the complexities of energy prediction to the promise of advanced technology, the book sets its sights on the game-changing potential of computer vision (CV) in the realm of renewable energy. Amidst the struggle to enhance sustainability across industries, CV technology emerges as a powerful ally, collecting invaluable data from digital photos and videos. This data proves instrumental in achieving better energy management, predicting factors affecting renewable energy, and optimizing overall sustainability. Readers, including researchers, academicians, and students, will find themselves immersed in a comprehensive understanding of the AI approaches and CV methodologies that hold the key to resolving the challenges faced by renewable energy systems.
Download or read book Solar Photovoltaic Energy written by Anne Labouret and published by IET. This book was released on 2010-12-17 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt: Providing designers, installers and managers with the tools and methods for the effective writing of technical reports and the ability to calculate, install and maintain the necessary components of photovoltaic energy.
Download or read book Advances in Electrical Control and Signal Systems written by Gayadhar Pradhan and published by Springer Nature. This book was released on 2020-07-01 with total page 1036 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents select proceedings of the International Conference on Advances in Electrical Control and Signal Systems (AECSS) 2019. The focus is on the current developments in control and signal systems in electrical engineering, and covers various topics such as power systems, energy systems, micro grid, smart grid, networks, fuzzy systems and their control. The book also discusses various properties and performance of signal systems and their applications in different fields. The contents of this book can be useful for students, researchers as well as professionals working in power and energy systems, and other related fields.
Download or read book Solar Energy Forecasting and Resource Assessment written by Jan Kleissl and published by Academic Press. This book was released on 2013-06-25 with total page 503 pages. Available in PDF, EPUB and Kindle. Book excerpt: Solar Energy Forecasting and Resource Assessment is a vital text for solar energy professionals, addressing a critical gap in the core literature of the field. As major barriers to solar energy implementation, such as materials cost and low conversion efficiency, continue to fall, issues of intermittency and reliability have come to the fore. Scrutiny from solar project developers and their financiers on the accuracy of long-term resource projections and grid operators’ concerns about variable short-term power generation have made the field of solar forecasting and resource assessment pivotally important. This volume provides an authoritative voice on the topic, incorporating contributions from an internationally recognized group of top authors from both industry and academia, focused on providing information from underlying scientific fundamentals to practical applications and emphasizing the latest technological developments driving this discipline forward. The only reference dedicated to forecasting and assessing solar resources enables a complete understanding of the state of the art from the world’s most renowned experts. Demonstrates how to derive reliable data on solar resource availability and variability at specific locations to support accurate prediction of solar plant performance and attendant financial analysis. Provides cutting-edge information on recent advances in solar forecasting through monitoring, satellite and ground remote sensing, and numerical weather prediction.
Download or read book Technology Toward Business Sustainability written by Bahaaeddin Alareeni and published by Springer Nature. This book was released on with total page 546 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Advances in Computational Intelligence written by Ignacio Rojas and published by Springer Nature. This book was released on 2023-11-03 with total page 723 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set LNCS 14134 and LNCS 14135 constitutes the refereed proceedings of the 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, held in Ponta Delgada, Portugal, during June 19–21, 2023. The 108 full papers presented in this two-volume set were carefully reviewed and selected from 149 submissions. The papers in Part I are organized in topical sections on advanced topics in computational intelligence; advances in artificial neural networks; ANN HW-accelerators; applications of machine learning in biomedicine and healthcare; and applications of machine learning in time series analysis. The papers in Part II are organized in topical sections on deep learning and applications; deep learning applied to computer vision and robotics; general applications of artificial intelligence; interaction with neural systems in both health and disease; machine learning for 4.0 industry solutions; neural networks in chemistry and material characterization; ordinal classification; real world applications of BCI systems; and spiking neural networks: applications and algorithms.
Download or read book Polygeneration Systems written by Francesco Calise and published by Academic Press. This book was released on 2021-09-22 with total page 453 pages. Available in PDF, EPUB and Kindle. Book excerpt: The support for polygeneration lies in the possibility of integrating different technologies into a single energy system, to maximize the utilization of both fossil and renewable fuels. A system that delivers multiple forms of energy to users, maximizing the overall efficiency makes polygeneration an emerging and viable option for energy consuming industries. Polygeneration Systems: Design, Processes and Technologies provides simple and advanced calculation techniques to evaluate energy, environmental and economic performance of polygeneration systems under analysis. With specific design guidelines for each type of polygeneration system and experimental performance data, referred both to single components and overall systems, this title covers all aspects of polygeneration from design to operation, optimization and practical implementation. Giving different aspects of both fossil and non-fossil fuel based polygeneration and the wider area of polygeneration processes, this book helps readers learn general principles to specific system design and development through analysis of case studies, examples, simulation characteristics and thermodynamic and economic data. Detailed economic data for technology to assist developing feasibility studies regarding the possible application of polygeneration technologies Offers a comprehensive list of all current numerical and experimental results of polygeneration available Includes simulation models, cost figures, demonstration projects and test standards for designers and researchers to validate their own models and/or to test the reliability of their results
Download or read book The 2030 Spike written by Colin Mason and published by Earthscan. This book was released on 2003 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: The clock is relentlessly ticking Our world teeters on a knife-edge between a peaceful and prosperous future for all, and a dark winter of death and destruction that threatens to smother the light of civilization. Within 30 years, in the 2030 decade, six powerful 'drivers' will converge with unprecedented force in a statistical spike that could tear humanity apart and plunge the world into a new Dark Age. Depleted fuel supplies, massive population growth, poverty, global climate change, famine, growing water shortages and international lawlessness are on a crash course with potentially catastrophic consequences. In the face of both doomsaying and denial over the state of our world, Colin Mason cuts through the rhetoric and reams of conflicting data to muster the evidence to illustrate a broad picture of the world as it is, and our possible futures. Ultimately his message is clear; we must act decisively, collectively and immediately to alter the trajectory of humanity away from catastrophe. Offering over 100 priorities for immediate action, The 2030 Spike serves as a guidebook for humanity through the treacherous minefields and wastelands ahead to a bright, peaceful and prosperous future in which all humans have the opportunity to thrive and build a better civilization. This book is powerful and essential reading for all people concerned with the future of humanity and planet earth.
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.
Download or read book A Field Guide to Dynamical Recurrent Networks written by John F. Kolen and published by John Wiley & Sons. This book was released on 2001-01-15 with total page 458 pages. Available in PDF, EPUB and Kindle. Book excerpt: Acquire the tools for understanding new architectures and algorithms of dynamical recurrent networks (DRNs) from this valuable field guide, which documents recent forays into artificial intelligence, control theory, and connectionism. This unbiased introduction to DRNs and their application to time-series problems (such as classification and prediction) provides a comprehensive overview of the recent explosion of leading research in this prolific field. A Field Guide to Dynamical Recurrent Networks emphasizes the issues driving the development of this class of network structures. It provides a solid foundation in DRN systems theory and practice using consistent notation and terminology. Theoretical presentations are supplemented with applications ranging from cognitive modeling to financial forecasting. A Field Guide to Dynamical Recurrent Networks will enable engineers, research scientists, academics, and graduate students to apply DRNs to various real-world problems and learn about different areas of active research. It provides both state-of-the-art information and a road map to the future of cutting-edge dynamical recurrent networks.
Download or read book Statistical Methods in the Atmospheric Sciences written by Daniel S. Wilks and published by Academic Press. This book was released on 2011-05-20 with total page 698 pages. Available in PDF, EPUB and Kindle. Book excerpt: This revised and expanded text explains the latest statistical methods that are being used to describe, analyze, test, and forecast atmospheric data. It features numerous worked examples, illustrations, equations, and exercises with separate solutions. The book will help advanced students and professionals understand and communicate what their data sets have to say, and make sense of the scientific literature in meteorology, climatology, and related disciplines.
