Like wind power and most other renewable energy sources besides reservoir hydroelectricity, solar photovoltaic (PV) power generation has the disadvantage of unintended variability. Residential PV generators in electric distribution systems pose a unique challenge to voltage control and general maintenance of stable operation within specific tolerances, because of the large number of distributed generators, combined with limited visibility and control. Short-term forecasting of solar irradiance for the predictive benefit of PV operators and other concerned parties is an active and developing field. However, short-term forecasting of the effects of fair-weather cumulus clouds is immature, and there has been little or no modeling of the spatiotemporal evolution of such clouds for use in forecasting. This dissertation describes research work performed toward this goal, including three key contributions to the forecasting art for PV production. The primary contribution is a method to produce computationally cost-effective reduced-order dynamical models of cumulus cloud evolution and forecasts therefrom that can potentially be performed within the computing platform of PV site's energy management system or inverter. Specifically, the method is a novel application of dynamic mode decomposition to clear-sky index forecasting of shadowing effects of convective fair-weather cumulus clouds. In the method, cloud dynamics are captured by sequences of visible-light photographic video frames. The method can be more easily applied to the modeling of cloud evolution than traditional fluid-based methods, and has decreased forecasting error compared to existing frozen-cloud advection methods. Its use is demonstrated for several actual fair-weather cumulus cloud image sequences and compared to advection-only forecasts. Second, a novel method for mapping color sky images of convective fair-weather cumuli to a scalar irradiance metric is presented. This method exploits the special structure of sky images in three-dimensional red-green-blue cartesian color space. The proposed metric is shown, by comparison with experimentally measured irradiance time series, to produce a more accurate clear-sky index in comparison with other methods. Third, a method for efficiently removing the bright glare of the solar disk from an entire image sequence in one operation, using proper orthogonal decomposition, is presented and its use is demonstrated. These contributions are presented in the greater context of irradiance forecasting as a fundamentally fluid-dynamical problem, and appendices are provided with detailed examinations into the current state of the art

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.

One of Mark Cuban’s top reads for better understanding A.I. (inc.com, 2021) Your comprehensive entry-level guide to machine learning While machine learning expertise doesn’t quite mean you can create your own Turing Test-proof android—as in the movie Ex Machina—it is a form of artificial intelligence and one of the most exciting technological means of identifying opportunities and solving problems fast and on a large scale. Anyone who masters the principles of machine learning is mastering a big part of our tech future and opening up incredible new directions in careers that include fraud detection, optimizing search results, serving real-time ads, credit-scoring, building accurate and sophisticated pricing models—and way, way more. Unlike most machine learning books, the fully updated 2nd Edition of Machine Learning For Dummies doesn't assume you have years of experience using programming languages such as Python (R source is also included in a downloadable form with comments and explanations), but lets you in on the ground floor, covering the entry-level materials that will get you up and running building models you need to perform practical tasks. It takes a look at the underlying—and fascinating—math principles that power machine learning but also shows that you don't need to be a math whiz to build fun new tools and apply them to your work and study. Understand the history of AI and machine learning Work with Python 3.8 and TensorFlow 2.x (and R as a download) Build and test your own models Use the latest datasets, rather than the worn out data found in other books Apply machine learning to real problems Whether you want to learn for college or to enhance your business or career performance, this friendly beginner's guide is your best introduction to machine learning, allowing you to become quickly confident using this amazing and fast-developing technology that's impacting lives for the better all over the world.

Fault detection, control, and forecasting have a vital role in renewable energy systems (Photovoltaics (PV) and wind turbines (WTs)) to improve their productivity, ef?ciency, and safety, and to avoid expensive maintenance. For instance, the main crucial and challenging issue in solar and wind energy production is the volatility of intermittent power generation due mainly to weather conditions. This fact usually limits the integration of PV systems and WTs into the power grid. Hence, accurately forecasting power generation in PV and WTs is of great importance for daily/hourly efficient management of power grid production, delivery, and storage, as well as for decision-making on the energy market. Also, accurate and prompt fault detection and diagnosis strategies are required to improve efficiencies of renewable energy systems, avoid the high cost of maintenance, and reduce risks of fire hazards, which could affect both personnel and installed equipment. This book intends to provide the reader with advanced statistical modeling, forecasting, and fault detection techniques in renewable energy systems.

Solar radiation data is important for a wide range of applications, e.g. in engineering, agriculture, health sector, and in many fields of the natural sciences. A few examples showing the diversity of applications may include: architecture and building design, e.g. air conditioning and cooling systems; solar heating system design and use; solar power generation; evaporation and irrigation; calculation of water requirements for crops; monitoring plant growth and disease control; skin cancer research.

Renewable Energy Forecasting: From Models to Applications provides an overview of the state-of-the-art of renewable energy forecasting technology and its applications. After an introduction to the principles of meteorology and renewable energy generation, groups of chapters address forecasting models, very short-term forecasting, forecasting of extremes, and longer term forecasting. The final part of the book focuses on important applications of forecasting for power system management and in energy markets. Due to shrinking fossil fuel reserves and concerns about climate change, renewable energy holds an increasing share of the energy mix. Solar, wind, wave, and hydro energy are dependent on highly variable weather conditions, so their increased penetration will lead to strong fluctuations in the power injected into the electricity grid, which needs to be managed. Reliable, high quality forecasts of renewable power generation are therefore essential for the smooth integration of large amounts of solar, wind, wave, and hydropower into the grid as well as for the profitability and effectiveness of such renewable energy projects. Offers comprehensive coverage of wind, solar, wave, and hydropower forecasting in one convenient volume Addresses a topic that is growing in importance, given the increasing penetration of renewable energy in many countries Reviews state-of-the-science techniques for renewable energy forecasting Contains chapters on operational applications

As an energy resource, solar energy provides a cleaner alternative to the conventional power generation systems and therefore solar energy has the potential to help achieve lower emissions standards as well to help provide domestic energy security. A major challenge, however, is the nondispatchability and variability of the solar resource which makes it necessary to develop forecasting methodologies in order to safely integrate with the electric grid. As dictated by current electricity markets, power generation is dispatched according to day-ahead unit commitment as well as 1-hour ahead and 15-minutes for load-following services. In order to integrate large penetration levels of solar energy into the current systems, forecasting at these time intervals are necessary. In this work, we develop and evaluate several solar irradiance forecast models for multiple-time horizons. The 1-day ahead forecasting models are based on forecasted elements from the National Weather System's (NWS) forecasting database (NDFD). The 1-hour ahead forecasting models are based on sky cover indices derived from ground measurements including solar and infrared radiometers as well as a sky imager and we also develop satellite-based models that utilize neural networks for time-series predictions. For very short-term forecasts of