Download or read book Forecasting U S Electricity Demand written by Adela Maria Bolet and published by Routledge. This book was released on 2019-08-30 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although the energy headlines of 1985 proclaim the waning of OPEC, the collapse of oil prices, and the demise of the nuclear power industry, few policy analysts are examining the dynamic challenges and opportunities that may confront the electric power industry during the remainder of this century. In this pioneering work, Adela Maria Bolet attempts to do exactly this, namely, to reconcile the differences among forecasters as to the future of electricity demand in the industrial, commercial, and residential sectors.

Download or read book Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast written by Federico Divina and published by MDPI. This book was released on 2021-08-30 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of data collectors in energy systems is growing more and more. For example, smart sensors are now widely used in energy production and energy consumption systems. This implies that huge amounts of data are generated and need to be analyzed in order to extract useful insights from them. Such big data give rise to a number of opportunities and challenges for informed decision making. In recent years, researchers have been working very actively in order to come up with effective and powerful techniques in order to deal with the huge amount of data available. Such approaches can be used in the context of energy production and consumption considering the amount of data produced by all samples and measurements, as well as including many additional features. With them, automated machine learning methods for extracting relevant patterns, high-performance computing, or data visualization are being successfully applied to energy demand forecasting.

Download or read book Modeling and Forecasting Electricity Loads and Prices written by Rafal Weron and published by John Wiley & Sons. This book was released on 2007-01-30 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers an in-depth and up-to-date review of different statistical tools that can be used to analyze and forecast the dynamics of two crucial for every energy company processes—electricity prices and loads. It provides coverage of seasonal decomposition, mean reversion, heavy-tailed distributions, exponential smoothing, spike preprocessing, autoregressive time series including models with exogenous variables and heteroskedastic (GARCH) components, regime-switching models, interval forecasts, jump-diffusion models, derivatives pricing and the market price of risk. Modeling and Forecasting Electricity Loads and Prices is packaged with a CD containing both the data and detailed examples of implementation of different techniques in Matlab, with additional examples in SAS. A reader can retrace all the intermediate steps of a practical implementation of a model and test his understanding of the method and correctness of the computer code using the same input data. The book will be of particular interest to the quants employed by the utilities, independent power generators and marketers, energy trading desks of the hedge funds and financial institutions, and the executives attending courses designed to help them to brush up on their technical skills. The text will be also of use to graduate students in electrical engineering, econometrics and finance wanting to get a grip on advanced statistical tools applied in this hot area. In fact, there are sixteen Case Studies in the book making it a self-contained tutorial to electricity load and price modeling and forecasting.

Download or read book Energy Time Series Forecasting written by Lars Dannecker and published by Springer. This book was released on 2015-08-06 with total page 241 pages. Available in PDF, EPUB and Kindle. Book excerpt: Lars Dannecker developed a novel online forecasting process that significantly improves how forecasts are calculated. It increases forecasting efficiency and accuracy, as well as allowing the process to adapt to different situations and applications. Improving the forecasting efficiency is a key pre-requisite for ensuring stable electricity grids in the face of an increasing amount of renewable energy sources. It is also important to facilitate the move from static day ahead electricity trading towards more dynamic real-time marketplaces. The online forecasting process is realized by a number of approaches on the logical as well as on the physical layer that we introduce in the course of this book. Nominated for the Georg-Helm-Preis 2015 awarded by the Technische Universität Dresden.

Download or read book Python based Deep Learning Methods for Energy Consumption Forecasting written by Josep Roman Cardell and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In a society where we do nothing but increase the use of electricity in our daily life, en-ergy consumption and the corresponding management is a major issue. The predictionof electric energy demand is a key component, for the power system operators, in themanagement of the electrical grid. The importance of forecasting a particular house-hold daily energy consumption does concern the end-user too, by reason of the designand sizing of a suitable renewable energy system and energy storage.The aim of this thesis is to develop and train a computing system capable of predict-ing, with best accuracy as possible, electricity consumption at household-level. Thispaper presents a Short Term Load Forecasting (STLF) with Artificial Neural Networks(ANN), which lead to accurate results in spite of the dwelling consumption unpre-dictability. The recorded data, containing the daily track of electricity consumption overa particular household from 2015 to 2018, was analysed. Subsequently, a study over theANN architecture and training algorithms was carried out in order to define a robustmodel. Furthermore, several experiments were conducted with different models, con-taining distinct inputs, aiming to compare the relevance of a diversity of parametersfor the network's training. Finally, the forecasting of the optimal models, created withthe insights collected over the whole research, was performed and compared in severalspecially selected time periods.The results showed how with the appropriate inputs and selection of hyperparame-ters, a shallow ANN can provide certain accuracy on the forecasting of electric energydemand. As well as a methodology to develop and train an artificial neural network.

Download or read book Data Mining and Machine Learning in Building Energy Analysis written by Frédéric Magoules and published by John Wiley & Sons. This book was released on 2016-02-08 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: The energy consumption of a building has, in recent years, become a determining factor during its design and construction. With carbon footprints being a growing issue, it is important that buildings be optimized for energy conservation and CO2 reduction. This book therefore presents AI models and optimization techniques related to this application. The authors start with a review of recent models for the prediction of building energy consumption: engineering methods, statistical methods, artificial intelligence methods, ANNs and SVMs in particular. The book then focuses on SVMs, by first applying them to building energy consumption, then presenting the principles and various extensions, and SVR. The authors then move on to RDP, which they use to determine building energy faults through simulation experiments before presenting SVR model reduction methods and the benefits of parallel computing. The book then closes by presenting some of the current research and advancements in the field.

Download or read book Forecasting and Assessing Risk of Individual Electricity Peaks written by Maria Jacob and published by Springer Nature. This book was released on 2019-09-25 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: The overarching aim of this open access book is to present self-contained theory and algorithms for investigation and prediction of electric demand peaks. A cross-section of popular demand forecasting algorithms from statistics, machine learning and mathematics is presented, followed by extreme value theory techniques with examples. In order to achieve carbon targets, good forecasts of peaks are essential. For instance, shifting demand or charging battery depends on correct demand predictions in time. Majority of forecasting algorithms historically were focused on average load prediction. In order to model the peaks, methods from extreme value theory are applied. This allows us to study extremes without making any assumption on the central parts of demand distribution and to predict beyond the range of available data. While applied on individual loads, the techniques described in this book can be extended naturally to substations, or to commercial settings. Extreme value theory techniques presented can be also used across other disciplines, for example for predicting heavy rainfalls, wind speed, solar radiation and extreme weather events. The book is intended for students, academics, engineers and professionals that are interested in short term load prediction, energy data analytics, battery control, demand side response and data science in general.

Download or read book Demand Forecasting for Electric Utilities written by Clark W. Gellings and published by . This book was released on 1992 with total page 552 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Renewable Energy Forecasting written by Georges Kariniotakis and published by Woodhead Publishing. This book was released on 2017-09-29 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Singular Spectrum Analysis with R written by Nina Golyandina and published by Springer. This book was released on 2018-06-14 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive and richly illustrated volume provides up-to-date material on Singular Spectrum Analysis (SSA). SSA is a well-known methodology for the analysis and forecasting of time series. Since quite recently, SSA is also being used to analyze digital images and other objects that are not necessarily of planar or rectangular form and may contain gaps. SSA is multi-purpose and naturally combines both model-free and parametric techniques, which makes it a very special and attractive methodology for solving a wide range of problems arising in diverse areas, most notably those associated with time series and digital images. An effective, comfortable and accessible implementation of SSA is provided by the R-package Rssa, which is available from CRAN and reviewed in this book. Written by prominent statisticians who have extensive experience with SSA, the book (a) presents the up-to-date SSA methodology, including multidimensional extensions, in language accessible to a large circle of users, (b) combines different versions of SSA into a single tool, (c) shows the diverse tasks that SSA can be used for, (d) formally describes the main SSA methods and algorithms, and (e) provides tutorials on the Rssa package and the use of SSA. The book offers a valuable resource for a very wide readership, including professional statisticians, specialists in signal and image processing, as well as specialists in numerous applied disciplines interested in using statistical methods for time series analysis, forecasting, signal and image processing. The book is written on a level accessible to a broad audience and includes a wealth of examples; hence it can also be used as a textbook for undergraduate and postgraduate courses on time series analysis and signal processing.

Download or read book International Energy Outlook written by and published by . This book was released on 1986 with total page 74 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Advanced Optimization Methods and Big Data Applications in Energy Demand Forecast written by Francisco A. Gómez Vela and published by . This book was released on 2021 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of data collectors in energy systems is growing more and more. For example, smart sensors are now widely used in energy production and energy consumption systems. This implies that huge amounts of data are generated and need to be analyzed in order to extract useful insights from them. Such big data give rise to a number of opportunities and challenges for informed decision making. In recent years, researchers have been working very actively in order to come up with effective and powerful techniques in order to deal with the huge amount of data available. Such approaches can be used in the context of energy production and consumption considering the amount of data produced by all samples and measurements, as well as including many additional features. With them, automated machine learning methods for extracting relevant patterns, high-performance computing, or data visualization are being successfully applied to energy demand forecasting. In light of the above, this Special Issue collects the latest research on relevant topics, in particular in energy demand forecasts, and the use of advanced optimization methods and big data techniques. Here, by energy, we mean any kind of energy, e.g., electrical, solar, microwave, or wind.

Download or read book Artificial Intelligence for Renewable Energy Systems written by Ajay Kumar Vyas and published by John Wiley & Sons. This book was released on 2022-03-02 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: ARTIFICIAL INTELLIGENCE FOR RENEWABLE ENERGY SYSTEMS Renewable energy systems, including solar, wind, biodiesel, hybrid energy, and other relevant types, have numerous advantages compared to their conventional counterparts. This book presents the application of machine learning and deep learning techniques for renewable energy system modeling, forecasting, and optimization for efficient system design. Due to the importance of renewable energy in today’s world, this book was designed to enhance the reader’s knowledge based on current developments in the field. For instance, the extraction and selection of machine learning algorithms for renewable energy systems, forecasting of wind and solar radiation are featured in the book. Also highlighted are intelligent data, renewable energy informatics systems based on supervisory control and data acquisition (SCADA); and intelligent condition monitoring of solar and wind energy systems. Moreover, an AI-based system for real-time decision-making for renewable energy systems is presented; and also demonstrated is the prediction of energy consumption in green buildings using machine learning. The chapter authors also provide both experimental and real datasets with great potential in the renewable energy sector, which apply machine learning (ML) and deep learning (DL) algorithms that will be helpful for economic and environmental forecasting of the renewable energy business. Audience The primary target audience includes research scholars, industry engineers, and graduate students working in renewable energy, electrical engineering, machine learning, information & communication technology.

Download or read book An Energy Demand Forecasting Model for Oregon written by Oregon. Energy Planning Program and published by . This book was released on 1977 with total page 86 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Scalable Local Short term Energy Consumption Forecasting written by Jay D Buckler and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Smart meter adoption rates are increasing globally and this has contributed to a rapid increase in the type and volume of data: communication, storage, and processing. These recent advances have created new opportunities for smart grid research, particularly in developing effective methods for processing big data. As power system industry moves towards adapting and implementing smart grid functions, energy demands forecasting is mandated at the distribution level to ensure the balance between energy supply and demand. Unlike system-level forecasting, short term energy demand forecasting at the distribution level needs to be highly scalable, due to the needs for collecting and processing energy demand data for a significant number of loads over a short time. This scalability requirement is magnified if the distribution level forecasting is to be performed centrally where system-level forecasting is being performed. In order to address these challenges, this thesis conducts a systematic study of the scalability and performance of time series forecasting techniques on smart meter data for distribution level short-term energy consumption. The conducted study is based on strategies to parallelize standard and online forecasting algorithms. The developed strategies are converted into algorithms to be implemented for performance evaluation. The performance of these algorithms is evaluated using data collected from several loads during different seasons. Test results demonstrate the challenges of including seasonality terms, and model training when using ARIMA based times series forecasting. Additional results show that the online algorithm achieves better scalability and shorter execution times when compared to the standard ARIMA implementation.

Download or read book Energy Demand Forecasting Issues written by Michael R. Jaske and published by . This book was released on 1985 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Energy Demand Forecasting written by United States. Congress. House. Committee on Science and Technology. Subcommittee on Investigations and Oversight and published by . This book was released on 1981 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: