Hybrid Intelligent Technologies in Energy Demand Forecasting


Book Description

This book is written for researchers and postgraduates who are interested in developing high-accurate energy demand forecasting models that outperform traditional models by hybridizing intelligent technologies. It covers meta-heuristic algorithms, chaotic mapping mechanism, quantum computing mechanism, recurrent mechanisms, phase space reconstruction, and recurrence plot theory. The book clearly illustrates how these intelligent technologies could be hybridized with those traditional forecasting models. This book provides many figures to deonstrate how these hybrid intelligent technologies are being applied to exceed the limitations of existing models.




Modeling and Forecasting Electricity Demand


Book Description

The master thesis of Kevin Berk develops a stochastic model for the electricity demand of small and medium-sized companies that is flexible enough so that it can be used for various business sectors. The model incorporates the grid load as an exogenous factor and seasonalities on a daily, weekly and yearly basis. It is demonstrated how the model can be used e.g. for estimating the risk of retail contracts. The uncertainty of electricity demand is an important risk factor for customers as well as for utilities and retailers. As a consequence, forecasting electricity load and its risk is now an integral component of the risk management for all market participants.




Forecasting and Assessing Risk of Individual Electricity Peaks


Book Description

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.




Energy for Sustainable Development


Book Description

Energy for Sustainable Development: Demand, Supply, Conversion and Management presents a comprehensive look at recent developments and provides guidance on energy demand, supply, analysis and forecasting of modern energy technologies for sustainable energy conversion. The book analyzes energy management techniques and the economic and environmental impact of energy usage and storage. Including modern theories and the latest technologies used in the conversion of energy for traditional fossil fuels and renewable energy sources, this book provides a valuable reference on recent innovations. Researchers, engineers and policymakers will find this book to be a comprehensive guide on modern theories and technologies for sustainable development.




Modeling and Forecasting Electricity Loads and Prices


Book Description

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.




Renewable Energy Forecasting


Book Description

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







Energy Demand: Facts and Trends


Book Description

The fIrst oil crisis of 1973-74 and the questions it raised in the economic and social fIelds drew attention to energy issues. Industrial societies, accustomed for two decades or more to energy sufficiently easy to produce and cheap to consume that it was thought to be inexhaustible, began to question their energy future. The studies undertaken at that time, and since, on a national, regional, or world level were over-optimistic. The problem seemed simple enough to solve. On the one hand, a certain number of resources: coal, the abundance of which was discovered, or rather rediscovered oil, source of all the problems ... In fact, the problems seemed to come, if not from oil itself (an easy explanation), then from those who produced it without really owning it, and from those who owned it without really control ling it natural gas, second only to oil and less compromised uranium, all of whose promises had not been kept, but whose resources were not in question solar energy, multiform and really inexhaustible thermonuclear fusion, and geothermal energy, etc. On the other hand, energy consumption, though excessive perhaps, was symbolic of progress, development, and increased well being. The originality of the energy policies set up since 1974 lies in the fact they no longer aimed to produce (or import) more, but to consume less. They sought, and still seek, what might be emphatically called the control of energy consump tion, or rather the control of energy demand.




Kernel Methods and Hybrid Evolutionary Algorithms in Energy Forecasting


Book Description

This book is a printed edition of the Special Issue "Kernel Methods and Hybrid Evolutionary Algorithms in Energy Forecasting" that was published in Energies




Energy Time Series Forecasting


Book Description

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.