Preface	6
	Acknowledgements	8
	Contents	10
	List of Figures	13
	List of Tables	16
	Chapter 1 Introduction	17
	Chapter 2 The European Electricity Market: A Market Study	26
	2.1 Current Developments in the European Electricity Market	27
	2.1.1 Structure of the European Electricity Market	27
	2.1.2 Development of Renewable Energy Sources in Europe and Germany	28
	2.1.3 Impact of Volatile Renewable Energy Sources	32
	2.1.4 How to Keep the Electricity Grid in Balance	35
	2.1.5 Extending the Transmission Grid and Energy Storage	40
	2.1.6 Demand-Side Management and Demand-Response	45
	2.1.7 Changes on the European Electricity Market	47
	2.1.8 Improvements in Forecasting Energy Demand and Renewable Supply	52
	2.2 The MIRABEL Project: Exploiting Demand and Supply Side Flexibility	56
	2.2.1 Flex-Offers	56
	2.2.2 Architecture of MIRABEL’s EDMS	58
	2.2.3 Basic and Advanced Use-Case	60
	2.3 Conclusion	61
	Chapter 3 The Current State of Energy Data Management and Forecasting	63
	3.1 Data Characteristics in the Energy Domain	64
	3.1.1 Seasonal Patterns	65
	3.1.2 Aggregation-Level-Dependent Predictability	67
	3.1.3 Time Series Context and Context Drifts	70
	3.1.4 Typical Data Characteristics of Energy Time Series	72
	3.2 Forecasting in the Energy Domain	73
	3.2.1 Forecast Models with Autoregressive Structures	73
	3.2.2 Exponential Smoothing	77
	3.2.3 Machine Learning Techniques	80
	3.3 Forecast Models Tailor-Made for the Energy Domain	82
	3.3.1 Exponential Smoothing for the Energy Domain	83
	3.3.2 A multi-equation forecast model using autoregression	84
	3.4 Estimation of Forecast Models	86
	3.4.1 Optimization of Derivable Functions	87
	3.4.2 Optimization of Arbitrary Functions	88
	3.4.3 Incremental Maintenance	90
	3.4.4 Local and Global Forecasting Algorithms Used in this book	91
	3.5 Challenges for Forecasting in the Energy Domain	96
	3.5.1 Exponentially Increasing Search Space	96
	3.5.2 Multi-Optima Search Space	97
	3.5.3 Continuous Evaluation and Estimation	98
	3.5.4 Further Challenges	99
	Chapter 4 The Online Forecasting Process: Efficiently Providing Accurate Predictions	100
	4.1 Requirements for Designing a Novel Forecasting Process	100
	4.2 The Current Forecasting Calculation Process	102
	4.3 The Online Forecasting Process	107
	4.3.1 The Forecast Model Repository	109
	4.3.2 A Flexible and Iterative Optimization for Forecast Models	112
	4.3.3 Evaluation	121
	4.4 Designing a Forecasting System for the New Electricity Market	126
	4.4.1 Integrating Forecasting into Data Management Systems	127
	4.4.2 Creating a Common Architecture for EDMSs	128
	4.4.3 Architecture of an Integrated Forecasting Component	130
	Chapter 5 Optimizations on the Logical Layer: Context-Aware Forecasting	133
	5.1 Context-Aware Forecast Model Materialization	134
	5.1.1 Case-based Reasoning and Context-Awareness in General	134
	5.1.2 The Context-Aware Forecast Model Repository	136
	5.1.3 Decision Criteria	137
	5.1.4 Preserving Forecast Models Using Time Series Context	139
	5.1.5 Forecast Model Retrieval and Assessment	144
	5.1.6 Evaluation	149
	5.2 A Framework for Efficiently Integrating External Information	153
	5.2.1 Separating the Forecast Model	154
	5.2.2 Reducing the Dimensionality of the External Information Model	155
	5.2.3 Determining the Final External Model	158
	5.2.4 Creating a Combined Forecast Model	160
	5.2.5 Integration with the Online Forecasting Process	161
	5.2.6 Experimental Evaluation	163
	5.3 Exploiting Hierarchical Time Series Structures	168
	5.3.1 Forecasting in Hierarchies	169
	5.3.2 Approach Outline	170
	5.3.3 Classification of Forecast Model Coefficients and Parameters	171
	5.3.4 Aggregation in Detail	173
	5.3.5 Applying the System to Real-World Forecast Models	176
	5.3.6 Hierarchical Communication	178
	5.3.7 Experimental Evaluation	179
	5.4 Conclusion	184
	Chapter 6 Optimizations on the Physical Layer: A Forecast-Model-Aware Time Series Storage	186
	6.1 Related Work	187
	6.1.1 Optimizing Time Series Management	187
	6.1.2 Special Purpose DMS	188
	6.1.3 Summarizing comparison	190
	6.2 Creating an Access-Pattern-Aware Time Series Storage	191
	6.2.1 Model Access Patterns	192
	6.2.2 Access-Pattern-Aware Storage	195
	6.3 Applying the Access-Pattern-Aware Storage to Real-World Forecast Models	200
	6.3.1 Optimized Storage for Single-Equation Models	200
	6.3.2 Optimized Storage for Multi-Equation Models	203
	6.4 Evaluation	206
	6.4.1 Single-Equation Models	207
	6.4.2 Multi-Equation Models	209
	6.5 Conclusion	214
	Chapter 7 Conclusion and Future Work	216
	References	221