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Forecasting Models of Electricity Prices

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ISBN: 9783038424154 9783038424147 Year: Pages: VIII, 250 DOI: 10.3390/books978-3-03842-414-7 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Environmental Engineering
Added to DOAB on : 2017-06-09 09:21:40
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The new competitive electricity markets make it imperative for companies related to electricity production and retail to have tools in place for energy offers. Price forecasting is critical when making offers in electricity markets, since it is considerably more complex than demand forecasting and the level of uncertainty is higher. Knowing electricity prices beforehand makes it possible for generators to determine their optimal production strategy in order to maximize their profit. Similarly, consumers and retailers can plan their consumption and produce energy bids to maximize their utility. Therefore, price forecasting is instrumental both for producers, retailers and consumers.

Weather & Climate Services for the Energy Industry

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ISBN: 9783319684178 9783319684185 Year: Pages: 197 DOI: 10.1007/978-3-319-68418-5 Language: English
Publisher: Palgrave Macmillan Grant: University of East Anglia; European Centre for Medium-Range Weather; EDF R&D; University of Reading
Subject: Meteorology and Climatology
Added to DOAB on : 2018-05-31 19:14:20
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This open access book showcases the burgeoning area of applied research at the intersection between weather and climate science and the energy industry. It illustrates how better communication between science and industry can help both sides. By opening a dialogue, scientists can understand the broader context for their work and the energy industry is able to keep track of and implement the latest scientific advances for more efficient and sustainable energy systems.Weather & Climate Services for the Energy Industry considers the lessons learned in establishing an ongoing discussion between the energy industry and the meteorological community and how its principles and practises can be applied elsewhere. This book will be a useful guiding resource for research and early career practitioners concerned with the energy industry and the new field of research known as energy meteorology.

Demographic and Socioeconomic Outcomes Across the Indigenous Australian Lifecourse

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Book Series: Research Monograph ISBN: 9781921862038 Year: Pages: 177 DOI: 10.26530/OAPEN_458941 Language: English
Publisher: ANU Press
Subject: Economics
Added to DOAB on : 2012-06-14 11:46:24
License: ANU Press

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Across almost all standard indicators, the Indigenous population of Australia has worse outcomes than the non-Indigenous population. Despite the abundance of statistics and a plethora of government reports on Indigenous outcomes, there is very little information on how Indigenous disadvantage accumulates or is mitigated through time at the individual level. The research that is available highlights two key findings. Firstly, that Indigenous disadvantage starts from a very early age and widens over time. Secondly, that the timing of key life events including education attendance, marriage, childbirth and retirement occur on average at different ages for the Indigenous compared to the non-Indigenous population. To target policy interventions that will contribute to meeting the Council of Australian Governments’ (COAG) Closing the Gap targets, it is important to understand and acknowledge the differences between the Indigenous and non-Indigenous lifecourse in Australia, as well as the factors that lead to variation within the Indigenous population.

Economics of Distributed Storage Systems : an economic analysis of arbitrage-maximizing storage systems at the end consumer level

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ISBN: 9783866445611 Year: Pages: XVIII, 163 p. DOI: 10.5445/KSP/1000019719 Language: ENGLISH
Publisher: KIT Scientific Publishing
Subject: Business and Management
Added to DOAB on : 2019-07-30 20:01:57
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Increasing the shares of Renewable Energy Sources (RES) and Distributed Energy Resources (DER) is one of the most important levers in many countries to cope with the environmental, political, and economic challenges of future energy supply. The underlying research question of this thesis is whether Distributed Storage Systems (DSS) at the end consumer level can economically foster the integration of intermittent and non-dispatchable resources by providing demand-side flexibility.

Hybrid Advanced Techniques for Forecasting in Energy Sector

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ISBN: 9783038972907 9783038972914 Year: Pages: 250 DOI: 10.3390/books978-3-03897-291-4 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science --- General and Civil Engineering
Added to DOAB on : 2018-10-19 10:39:42
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Accurate forecasting performance in the energy sector is a primary factor in the modern restructured power market, accomplished by any novel advanced hybrid techniques. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated by factors such as seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. To comprehensively address this issue, it is insufficient to concentrate only on simply hybridizing evolutionary algorithms with each other, or on hybridizing evolutionary algorithms with chaotic mapping, quantum computing, recurrent and seasonal mechanisms, and fuzzy inference theory in order to determine suitable parameters for an existing model. It is necessary to also consider hybridizing or combining two or more existing models (e.g., neuro-fuzzy model, BPNN-fuzzy model, seasonal support vector regression–chaotic quantum particle swarm optimization (SSVR-CQPSO), etc.). These advanced novel hybrid techniques can provide more satisfactory energy forecasting performances.This book aimed to attract researchers with an interest in the research areas described above. Specifically, we were interested in contributions towards recent developments, i.e., hybridizing or combining any advanced techniques in energy forecasting, with the superior capabilities over the traditional forecasting approaches, with the ability to overcome some embedded drawbacks, and with the very superiority to achieve significant improved forecasting accuracy.

Short-Term Load Forecasting by Artificial Intelligent Technologies

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ISBN: 9783038975823 / 9783038975830 Year: Pages: 444 DOI: 10.3390/books978-3-03897-583-0 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science
Added to DOAB on : 2019-01-29 10:55:39
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In last few decades, short-term load forecasting (STLF) has been one of the most important research issues for achieving higher efficiency and reliability in power system operation, to facilitate the minimization of its operation cost by providing accurate input to day-ahead scheduling, contingency analysis, load flow analysis, planning, and maintenance of power systems. There are lots of forecasting models proposed for STLF, including traditional statistical models (such as ARIMA, SARIMA, ARMAX, multi-variate regression, Kalman filter, exponential smoothing, and so on) and artificial-intelligence-based models (such as artificial neural networks (ANNs), knowledge-based expert systems, fuzzy theory and fuzzy inference systems, evolutionary computation models, support vector regression, and so on). Recently, due to the great development of evolutionary algorithms (EA) and novel computing concepts (e.g., quantum computing concepts, chaotic mapping functions, and cloud mapping process, and so on), many advanced hybrids with those artificial-intelligence-based models are also proposed to achieve satisfactory forecasting accuracy levels. In addition, combining some superior mechanisms with an existing model could empower that model to solve problems it could not deal with before; for example, the seasonal mechanism from the ARIMA model is a good component to be combined with any forecasting models to help them to deal with seasonal problems.

Solar Particle Radiation Storms Forecasting and Analysis: The HESPERIA HORIZON 2020 Project and Beyond

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Book Series: Astrophysics and Space Science Library ISSN: 0067-0057 ISBN: 9783319600505 9783319600512 Year: Volume: 444 Pages: 203 DOI: https://doi.org/10.1007/978-3-319-60051-2 Language: English
Publisher: Springer Grant: Horizon 2020 Framework Programme
Subject: Nuclear Physics
Added to DOAB on : 2018-07-18 15:14:22
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Solar energetic particles (SEPs) emitted from the Sun are a major space weather hazard motivating the development of predictive capabilities. This book presents the results and findings of the HESPERIA (High Energy Solar Particle Events forecasting and Analysis) project of the EU HORIZON 2020 programme. It discusses the forecasting operational tools developed within the project, and presents progress to SEP research contributed by HESPERIA both from the observational as well as the SEP modelling perspective. Using multi-frequency observational data and simulations HESPERIA investigated the chain of processes from particle acceleration in the corona, particle transport in the magnetically complex corona and interplanetary space, to the detection near 1 AU. The book also elaborates on the unique software that has been constructed for inverting observations of relativistic SEPs to physical parameters that can be compared with space-borne measurements at lower energies. Introductory and pedagogical material included in the book make it accessible to students at graduate level and will be useful as background material for Space Physics and Space Weather courses with emphasis on Solar Energetic Particle Event Forecasting and Analysis.

Wind Turbines

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ISBN: 9783038973607 / 9783038973614 Year: Pages: 328 DOI: 10.3390/books978-3-03897-361-4 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Electrical and Nuclear Engineering
Added to DOAB on : 2019-01-11 09:36:07
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This issue is a continuation of the previous successful Special Issue “Wind Turbines 2013”. Similarly, this issue also focuses on recent advances in the wind energy sector on a wide range of topics, including: wind resource mapping, wind intermittency issues, aerodynamics, foundations, aeroelasticity, wind turbine technologies, control of wind turbines, diagnostics, generator concepts including gearless concepts, power electronic converters, grid interconnection, ride-through operation, protection, wind farm layouts - optimization and control, reliability, operations and maintenance, effects of wind farms on local and global climate, wind power stations, smart-grid and micro-grid related to wind turbine operation.

Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting

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ISBN: 9783038972860 9783038972877 Year: Pages: 250 DOI: 10.3390/books978-3-03897-287-7 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science
Added to DOAB on : 2018-10-19 11:45:03
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More accurate and precise energy demand forecasts are required when energy decisions are made in a competitive environment. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated. Examples include seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. These forecasting models have resulted in an over-reliance on the use of informal judgment and higher expenses when lacking the ability to determine data characteristics and patterns. The hybridization of optimization methods and superior evolutionary algorithms can provide important improvements via good parameter determinations in the optimization process, which is of great assistance to actions taken by energy decision-makers.This book aimed to attract researchers with an interest in the research areas described above. Specifically, it sought contributions to the development of any hybrid optimization methods (e.g., quadratic programming techniques, chaotic mapping, fuzzy inference theory, quantum computing, etc.) with advanced algorithms (e.g., genetic algorithms, ant colony optimization, particle swarm optimization algorithm, etc.) that have superior capabilities over the traditional optimization approaches to overcome some embedded drawbacks, and the application of these advanced hybrid approaches to significantly improve forecasting accuracy.

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