Forecasting Demand For Electric Energy
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Author | : Adela Maria Bolet |
Publisher | : Routledge |
Total Pages | : 274 |
Release | : 2019-08-30 |
Genre | : Political Science |
ISBN | : 0429711468 |
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.
Author | : Lyna L. Wiggins |
Publisher | : |
Total Pages | : 608 |
Release | : 1981 |
Genre | : Dissertations, Academic |
ISBN | : |
Author | : Clark W. Gellings |
Publisher | : |
Total Pages | : 552 |
Release | : 1992 |
Genre | : Technology & Engineering |
ISBN | : |
Author | : Wei-Chiang Hong |
Publisher | : Springer Science & Business Media |
Total Pages | : 203 |
Release | : 2013-03-12 |
Genre | : Business & Economics |
ISBN | : 1447149688 |
As industrial, commercial, and residential demands increase and with the rise of privatization and deregulation of the electric energy industry around the world, it is necessary to improve the performance of electric operational management. Intelligent Energy Demand Forecasting offers approaches and methods to calculate optimal electric energy allocation to reach equilibrium of the supply and demand. Evolutionary algorithms and intelligent analytical tools to improve energy demand forecasting accuracy are explored and explained in relation to existing methods. To provide clearer picture of how these hybridized evolutionary algorithms and intelligent analytical tools are processed, Intelligent Energy Demand Forecasting emphasizes on improving the drawbacks of existing algorithms. Written for researchers, postgraduates, and lecturers, Intelligent Energy Demand Forecasting helps to develop the skills and methods to provide more accurate energy demand forecasting by employing novel hybridized evolutionary algorithms and intelligent analytical tools.
Author | : Economic Sciences Corporation |
Publisher | : |
Total Pages | : 46 |
Release | : 1975 |
Genre | : Electric utilities |
ISBN | : |
Author | : Michigan. Governor's Advisory Commission on Electric Power Alternatives |
Publisher | : |
Total Pages | : 54 |
Release | : 1976 |
Genre | : |
ISBN | : |
Author | : Maria Jacob |
Publisher | : Springer Nature |
Total Pages | : 108 |
Release | : 2019-09-25 |
Genre | : Mathematics |
ISBN | : 303028669X |
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.
Author | : Wei-Chiang Hong |
Publisher | : Springer Nature |
Total Pages | : 179 |
Release | : 2020-01-01 |
Genre | : Business & Economics |
ISBN | : 3030365298 |
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.
Author | : Stanford University. Energy Modeling Forum |
Publisher | : |
Total Pages | : 430 |
Release | : 1980 |
Genre | : Electric utilities |
ISBN | : |
Author | : Economic Sciences Corporation |
Publisher | : |
Total Pages | : 538 |
Release | : 1975 |
Genre | : Electric Utilities |
ISBN | : |