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.

It is common wisdom that gathering a variety of views and inputs improves the process of decision making, and, indeed, underpins a democratic society. Dubbed “ensemble learning” by researchers in computational intelligence and machine learning, it is known to improve a decision system’s robustness and accuracy. Now, fresh developments are allowing researchers to unleash the power of ensemble learning in an increasing range of real-world applications. Ensemble learning algorithms such as “boosting” and “random forest” facilitate solutions to key computational issues such as face recognition and are now being applied in areas as diverse as object tracking and bioinformatics. Responding to a shortage of literature dedicated to the topic, this volume offers comprehensive coverage of state-of-the-art ensemble learning techniques, including the random forest skeleton tracking algorithm in the Xbox Kinect sensor, which bypasses the need for game controllers. At once a solid theoretical study and a practical guide, the volume is a windfall for researchers and practitioners alike.

The cost of operating a building far exceeds the cost of constructing it, and yet until recently little attention was paid to the impact of solar radiation on the costs of heating, cooling and ventilation. And now that there has been a surge in interest in energy efficiency and solar design, architects and designers need a practical guide to the modelling and application of solar energy data. There are many different models and techniques available for calculating the distribution of solar radiation on and in buildings, and these algorithms vary considerably in scope, accuracy and complexity. This book demonstrates which of these predictive tools gives the best results in different circumstances, including explaining which models can be best used in different parts of the world. The author has had over twenty-five years of experience of dealing with solar energy data from four continents and has used that experience in this book to show the development not just of knowledge but also the growing sophistication of the models available to apply it.

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.

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.

This book provides an introduction to forecasting methods for renewable energy sources integrated with existing grid. It consists of two sections; the first one is on the generation side forecasting methods, while the second section deals with the different ways of load forecasting. It broadly includes artificial intelligence, machine learning, hybrid techniques and other state-of-the-art techniques for renewable energy and load predictions. The book reflects the state of the art in distributed generation system and future microgrids and covers theory, algorithms, simulations and case studies. It offers invaluable insights through this valuable resource to students and researchers working in the fields of renewable energy, integration of renewable energy with existing grid and electrical distribution network.

Statistical Postprocessing of Ensemble Forecasts brings together chapters contributed by international subject-matter experts describing the current state of the art in the statistical postprocessing of ensemble forecasts. The book illustrates the use of these methods in several important applications including weather, hydrological and climate forecasts, and renewable energy forecasting. After an introductory section on ensemble forecasts and prediction systems, the second section of the book is devoted to exposition of the methods available for statistical postprocessing of ensemble forecasts: univariate and multivariate ensemble postprocessing are first reviewed by Wilks (Chapters 3), then Schefzik and Möller (Chapter 4), and the more specialized perspective necessary for postprocessing forecasts for extremes is presented by Friederichs, Wahl, and Buschow (Chapter 5). The second section concludes with a discussion of forecast verification methods devised specifically for evaluation of ensemble forecasts (Chapter 6 by Thorarinsdottir and Schuhen). The third section of this book is devoted to applications of ensemble postprocessing. Practical aspects of ensemble postprocessing are first detailed in Chapter 7 (Hamill), including an extended and illustrative case study. Chapters 8 (Hemri), 9 (Pinson and Messner), and 10 (Van Schaeybroeck and Vannitsem) discuss ensemble postprocessing specifically for hydrological applications, postprocessing in support of renewable energy applications, and postprocessing of long-range forecasts from months to decades. Finally, Chapter 11 (Messner) provides a guide to the ensemble-postprocessing software available in the R programming language, which should greatly help readers implement many of the ideas presented in this book. Edited by three experts with strong and complementary expertise in statistical postprocessing of ensemble forecasts, this book assesses the new and rapidly developing field of ensemble forecast postprocessing as an extension of the use of statistical corrections to traditional deterministic forecasts. Statistical Postprocessing of Ensemble Forecasts is an essential resource for researchers, operational practitioners, and students in weather, seasonal, and climate forecasting, as well as users of such forecasts in fields involving renewable energy, conventional energy, hydrology, environmental engineering, and agriculture. - Consolidates, for the first time, the methodologies and applications of ensemble forecasts in one succinct place - Provides real-world examples of methods used to formulate forecasts - Presents the tools needed to make the best use of multiple model forecasts in a timely and efficient manner