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IEA Wind recommended practice for the implementation of renewable energy forecasting solutions
Title
IEA Wind recommended practice for the implementation of renewable energy forecasting solutions [electronic resource] / Corinna Möhrlen [and more].
ISBN
9780443186820
0443186820
9780443186813
0443186812
Published
London, UK : Academic Press, [2023]
Physical Description
1 online resource.
Local Notes
Access is available to the Yale community.
Access and use
Access restricted by licensing agreement.
Variant and related titles
Elsevier ScienceDirect All Books. OCLC KB.
Other formats
ebook version :
Original
Print version: MOHRLEN, CORINNA. IEA WIND RECOMMENDED PRACTICE FOR THE IMPLEMENTATION OF RENEWABLE ENERGY FORECASTING... SOLUTIONS. [S.l.] : ELSEVIER ACADEMIC PRESS, 2022
Format
Books / Online
Language
English
Added to Catalog
June 06, 2023
Series
Wind energy engineering
Bibliography
Includes bibliographical references and index.
Contents
Front Cover
IEA Wind Recommended Practice for the Implementation of Renewable Energy Forecasting Solutions
Copyright
Contents
List of figures
List of tables
Biography
Dr. Corinna M hrlen
Dr. John W. Zack
Dr. Gregor Giebel
Preface
About the IEA Wind TCP and Task 36 and 51
Part 1 Forecast solution selection process
1 Forecast solution selection process
1.1 Before you start reading
1.2 Background and introduction
1.3 Objectives
1.4 Definitions
2 Initial considerations
2.1 Tackling the task of engaging a forecaster for the first time
2.2 Purpose and requirements of a forecasting solution
2.3 Adding uncertainty forecasts to forecasting solutions
2.4 Information table for specific topic targets
3 Decision support tool
3.1 Initial forecast system planning
3.2 IT infrastructure considerations
3.2.1 IT requirements for single versus multiple forecast vendors
3.2.2 IT requirements for deterministic versus probabilistic forecasts
3.3 Establishment of a requirement list
3.3.1 Requirement list
3.4 Short-term solution
3.5 Long-term solution
3.6 Going forward with an established IT system
3.7 Complexity level of the existing IT solution
3.8 Selection of a new vendor versus benchmarking existing vendor
3.9 RFP evaluation criteria for a forecast solution
3.9.1 Forecast solution type
3.9.1.1 Single versus multiple forecast providers
3.9.1.2 Deterministic versus probabilistic
3.9.1.3 Forecast horizons
3.9.2 Vendor capabilities
3.9.2.1 Experience and reliability
3.9.2.2 Ability to maintain state-of-the-art performance
3.9.2.3 Performance incentive schemes
3.9.3 Evaluation of services
3.9.3.1 Price versus value and quality
3.9.3.2 Forecast performance
3.9.3.3 Solution characteristics
3.9.3.4 Support structure.
3.9.3.5 Redundancy structure
3.9.3.6 Escalation structure
3.10 Forecast methodology selection for use of probabilistic forecasts
3.10.1 Definitions of uncertainty
3.10.2 Uncertainty forecasting methods
3.10.3 Training tools for ensemble forecasting
3.10.4 Applications of uncertainty forecasts in the energy industry
3.10.5 Visualization of forecast uncertainty
4 Data communication
4.1 Terminology
4.2 Data description
4.2.1 LEVEL 1
data description
4.3 Data format and exchange
4.3.1 LEVEL 1 data format and exchange
4.3.2 LEVEL 2
data format and exchange
4.4 Sample formatted template files and schemas
5 Concluding remarks
Part 2 Designing and executing forecasting benchmarks and trials
6 Designing and executing benchmarks and trials
6.1 Before you start reading
6.2 Background and introduction
6.3 Definitions
6.3.1 Renewable energy forecast benchmark
6.3.2 Renewable energy forecast trial
6.4 Objectives
7 Initial considerations
7.1 Deciding whether to conduct a trial or benchmark
7.2 Benefits of trials and benchmarks
7.3 Limitations with trials and benchmarks
7.4 Time lines and forecast periods in a trial or benchmark
7.5 1-Page ``cheat sheet'' checklist
8 Conducting a benchmark or trial
8.1 Phase 1: preparation
8.1.1 Key considerations in the preparation phase
8.1.2 Metadata gathering in the preparation phase
8.1.3 Historical data gathering in the preparation phase
8.1.4 IT/data considerations in the preparation phase
8.1.5 Communication in the preparation phase
8.1.6 Test run in the preparation phase
8.2 Phase 2: During benchmark/trial
8.2.1 Communication during the b/t
8.2.2 Forecast validation and reporting during the b/t
8.3 Phase 3: Post trial or benchmark
8.3.1 Communication at the end of the b/t.
8.3.2 Forecast validation &
reporting after the b/t
9 Considerations for probabilistic benchmarks and trials
9.1 Preparation phase challenges for probabilistic b/t
9.2 Evaluation challenges for probabilistic b/t
10 Best practice recommendations for benchmarks/trials
10.1 Best practice for b/t
10.2 Pitfalls to avoid
Part 3 Forecast solution evaluation
11 Forecast solution evaluation
11.1 Before you start reading
11.2 Background and introduction
12 Overview of evaluation uncertainty
12.1 Representativeness
12.1.1 Size and composition of the evaluation sample
12.1.2 Data quality
12.1.3 Forecast submission control
12.1.4 Process information dissemination
12.2 Significance
12.2.1 Quantification of uncertainty
12.2.1.1 Method 1: repeating the evaluation task
12.2.1.2 Method 2: bootstrap resampling
12.3 Relevance
13 Measurement data processing and control
13.1 Uncertainty of instrumentation signals and measurements
13.2 Measurement data reporting and collection
13.2.1 Non-weather related production reductions
13.2.2 Aggregation of measurement data in time and space
13.3 Measurement data processing and archiving
13.4 Quality assurance and quality control
14 Assessment of forecast performance
14.1 Forecast attributes at metric selection
14.1.1 ``Typical'' error metrics
14.1.2 Outlier/extreme error
14.1.3 Empirical error distribution
14.1.4 Binary or multi-criteria events
14.2 Prediction intervals and predictive distributions
14.3 Probabilistic forecast assessment methods
14.3.1 Brier scores
14.3.2 Ranked probability (skill) score (RP(S)s)
14.3.2.1 The continuous ranked probability skill and energy score
14.3.2.2 Logarithmic and variogram scoring rules
14.3.3 Reliability measures
14.3.3.1 Rank histogram.
14.3.3.2 Reliability (calibration) diagram
14.3.4 Event discrimination ability: relative operating characteristic (ROC)
14.3.5 Uncertainty in forecasts: R ny entropy
14.4 Metric-based forecast optimization
14.5 Post-processing of ensemble forecasts
15 Best practice recommendations for forecast evaluation
15.1 Developing an evaluation framework
15.1.1 Scoring rules for comparison of forecast types
15.1.2 Forecast and forecast error analysis
15.1.3 Choice of deterministic verification methods
15.1.3.1 Dichotomous event evaluation
15.1.3.2 Analyzing forecast error spread with box and wiskers plots
15.1.3.3 Visualizing the error frequency distribution with histograms
15.1.4 Specific probabilistic forecast verification
15.1.4.1 Choice of application for benchmarking probabilistic forecasts
15.1.5 Establishing a cost function or evaluation matrix
15.1.5.1 Evaluation matrix
15.2 Operational forecast value maximization
15.2.1 Performance monitoring
15.2.1.1 Importance of performance monitoring for different time periods
15.2.2 Forecast diagnostics and continuous improvement
15.2.3 Maximization of forecast value
15.2.4 Maintaining state-of-the-art performance
15.2.4.1 Significance test for new developments
15.2.5 Incentivization
15.3 Evaluation of benchmarks and trials
15.3.1 Applying the principles of representativeness, significance, and relevance
15.3.2 Evaluation preparation in the execution phase
15.3.3 Performance analysis in the evaluation phase
15.3.4 Evaluation examples from a benchmark
15.4 Use cases
15.4.1 Energy trading and balancing
15.4.1.1 Forecast error cost functions
15.4.2 General ramping forecasts
15.4.2.1 Amplitude versus phase
15.4.2.2 Costs of false alarms
15.4.3 Evaluation of probabilistic ramp forecasts for reserve allocation.
15.4.3.1 Definition of error conditions for the forecast
Part 4 Meteorological and power data requirements for real-time forecasting applications
16 Meteorological and power data requirements for real-time forecasting applications
16.1 Before you start reading
16.2 Background and introduction
16.3 Structure and recommended use
17 Use and application of real-time meteorological measurements
17.1 Application-specific requirements
17.1.1 Application-specific requirements for meteorological data
17.1.2 Applications in system operation, balancing and trading
17.1.3 Applications in wind turbine and wind farm operation
17.1.4 Solar/PV plant operation
17.2 Available and applicable standards for real-time meteorological and power measurements
17.2.1 Standards and guidelines for wind measurements
17.2.2 Standards and guidelines for solar measurements
17.3 Standards and guidelines for general meteorological measurements
17.4 Data communication
18 Meteorological instrumentation for real-time operation
18.1 Instrumentation for wind projects
18.1.1 Cup anemometers
18.1.2 Sonic and ultra-sonic anemometers
18.1.3 Remote sensing devices
18.1.4 Met mast sensor deployment
18.1.5 Nacelle sensor deployment
18.2 Instrumentation for solar projects
18.2.1 Point measurements
18.2.2 All sky imagers
18.2.3 Satellite data
19 Power measurements for real-time operation
19.1 Live power and related measurements
19.2 Measurement systems
19.2.1 Connection-point meters
19.2.2 Wind power SCADA systems
19.2.3 Solar power SCADA systems
19.3 Power available signals
19.3.1 Embedded wind and solar ``behind the meter''
19.4 Live power data in forecasting
19.4.1 Specifics for producers of forecasts
19.4.2 Specifics for consumers/users of forecasts
19.5 Summary of best practices.
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