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E-grāmata: Energy Time Series Forecasting: Efficient and Accurate Forecasting of Evolving Time Series from the Energy Domain

  • Formāts: PDF+DRM
  • Izdošanas datums: 06-Aug-2015
  • Izdevniecība: Springer Vieweg
  • Valoda: eng
  • ISBN-13: 9783658110390
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Energy Time Series Forecasting: Efficient and Accurate Forecasting of Evolving Time Series from the Energy DomainPalielināt
  • Formāts: PDF+DRM
  • Izdošanas datums: 06-Aug-2015
  • Izdevniecība: Springer Vieweg
  • Valoda: eng
  • ISBN-13: 9783658110390
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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.

The European Electricity Market: A Market Study.- The Current State of Energy Data Management and Forecasting.- The Online Forecasting Process: Efficiently Providing Accurate Predictions.- Optimizations on the Logical Layer: Context-Aware Forecasting.- Optimizations on the Physical Layer: A Forecast-Model-Aware Storage.
1 Introduction
1(10)
2 The European Electricity Market: A Market Study
11(38)
2.1 Current Developments in the European Electricity Market
12(29)
2.1.1 Structure of the European Electricity Market
12(1)
2.1.2 Development of Renewable Energy Sources in Europe and Germany
13(4)
2.1.3 Impact of Volatile Renewable Energy Sources
17(3)
2.1.4 How to Keep the Electricity Grid in Balance
20(5)
2.1.5 Extending the Transmission Grid and Energy Storage
25(5)
2.1.6 Demand-Side Management and Demand-Response
30(2)
2.1.7 Changes on the European Electricity Market
32(5)
2.1.8 Improvements in Forecasting Energy Demand and Renewable Supply
37(4)
2.2 The MIRABEL Project: Exploiting Demand and Supply Side Flexibility
41(5)
2.2.1 Flex-Offers
41(2)
2.2.2 Architecture of Mirabel's Edms
43(2)
2.2.3 Basic and Advanced Use-Case
45(1)
2.3 Conclusion
46(3)
3 The Current State of Energy Data Management and Forecasting
49(38)
3.1 Data Characteristics in the Energy Domain
50(9)
3.1.1 Seasonal Patterns
51(2)
3.1.2 Aggregation-Level-Dependent Predictability
53(3)
3.1.3 Time Series Context and Context Drifts
56(2)
3.1.4 Typical Data Characteristics of Energy Time Series
58(1)
3.2 Forecasting in the Energy Domain
59(9)
3.2.1 Forecast Models with Autoregressive Structures
59(4)
3.2.2 Exponential Smoothing
63(3)
3.2.3 Machine Learning Techniques
66(2)
3.3 Forecast Models Tailor-Made for the Energy Domain
68(4)
3.3.1 Exponential Smoothing for the Energy Domain
69(1)
3.3.2 A multi-equation forecast model using autoregression
70(2)
3.4 Estimation of Forecast Models
72(10)
3.4.1 Optimization of Derivable Functions
73(1)
3.4.2 Optimization of Arbitrary Functions
74(2)
3.4.3 Incremental Maintenance
76(1)
3.4.4 Local and Global Forecasting Algorithms Used in this book
77(5)
3.5 Challenges for Forecasting in the Energy Domain
82(5)
3.5.1 Exponentially Increasing Search Space
82(1)
3.5.2 Multi-Optima Search Space
83(1)
3.5.3 Continuous Evaluation and Estimation
84(1)
3.5.4 Further Challenges
85(2)
4 The Online Forecasting Process: Efficiently Providing Accurate Predictions
87(34)
4.1 Requirements for Designing a Novel Forecasting Process
87(2)
4.2 The Current Forecasting Calculation Process
89(5)
4.3 The Online Forecasting Process
94(19)
4.3.1 The Forecast Model Repository
96(3)
4.3.2 A Flexible and Iterative Optimization for Forecast Models
99(9)
4.3.3 Evaluation
108(5)
4.4 Designing a Forecasting System for the New Electricity Market
113(8)
4.4.1 Integrating Forecasting into Data Management Systems
114(1)
4.4.2 Creating a Common Architecture for EDMSs
115(2)
4.4.3 Architecture of an Integrated Forecasting Component
117(4)
5 Optimizations on the Logical Layer: Context-Aware Forecasting
121(54)
5.1 Context-Aware Forecast Model Materialization
122(19)
5.1.1 Case-based Reasoning and Context-Awareness in General
122(2)
5.1.2 The Context-Aware Forecast Model Repository
124(1)
5.1.3 Decision Criteria
125(2)
5.1.4 Preserving Forecast Models Using Time Series Context
127(5)
5.1.5 Forecast Model Retrieval and Assessment
132(5)
5.1.6 Evaluation
137(4)
5.2 A Framework for Efficiently Integrating External Information
141(15)
5.2.1 Separating the Forecast Model
142(1)
5.2.2 Reducing the Dimensionality of the External Information Model
143(3)
5.2.3 Determining the Final External Model
146(2)
5.2.4 Creating a Combined Forecast Model
148(1)
5.2.5 Integration with the Online Forecasting Process
149(2)
5.2.6 Experimental Evaluation
151(5)
5.3 Exploiting Hierarchical Time Series Structures
156(16)
5.3.1 Forecasting in Hierarchies
157(1)
5.3.2 Approach Outline
158(1)
5.3.3 Classification of Forecast Model Coefficients and Parameters
159(2)
5.3.4 Aggregation in Detail
161(3)
5.3.5 Applying the System to Real-World Forecast Models
164(2)
5.3.6 Hierarchical Communication
166(1)
5.3.7 Experimental Evaluation
167(5)
5.4 Conclusion
172(3)
6 Optimizations on the Physical Layer: A Forecast-Model-Aware Time Series Storage
175(30)
6.1 Related Work
176(4)
6.1.1 Optimizing Time Series Management
176(1)
6.1.2 Special Purpose DMS
177(2)
6.1.3 Summarizing comparison
179(1)
6.2 Creating an Access-Pattern-Aware Time Series Storage
180(9)
6.2.1 Model Access Patterns
181(3)
6.2.2 Access-Pattern-Aware Storage
184(5)
6.3 Applying the Access-Pattern-Aware Storage to Real-World Forecast Models
189(6)
6.3.1 Optimized Storage for Single-Equation Models
189(3)
6.3.2 Optimized Storage for Multi-Equation Models
192(3)
6.4 Evaluation
195(8)
6.4.1 Single-Equation Models
196(2)
6.4.2 Multi-Equation Models
198(5)
6.5 Conclusion
203(2)
7 Conclusion and Future Work
205(6)
References 211
Lars Dannecker holds a diploma in media computer science from the Technische Universität Dresden and is pursuing a doctorate as a member of the Database Technology Group led by Prof. Dr.-Ing. Wolfgang Lehner. 
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