IEC TR 63043:2020

IEC TR 63043 ®
Edition 1.0 2020-11
TECHNICAL
REPORT
colour
inside
Renewable energy power forecasting technology
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IEC TR 63043 ®
Edition 1.0 2020-11
TECHNICAL
REPORT
colour
inside
Renewable energy power forecasting technology

INTERNATIONAL
ELECTROTECHNICAL
COMMISSION
ICS 29.020 ISBN 978-2-8322-9079-8

– 2 – IEC TR 63043:2020  IEC 2020
CONTENTS
FOREWORD . 7
INTRODUCTION . 9
1 Scope . 10
2 Normative references . 10
3 Terms, definitions and abbreviated terms . 10
3.1 Terms and definitions . 11
3.2 Abbreviated terms . 13
4 General introduction to renewable energy power forecasting . 15
4.1 History of RPF . 15
4.1.1 General . 15
4.1.2 Development of wind power forecasting . 16
4.1.3 Development of PV power forecasting . 17
4.2 Use of RPF . 17
4.2.1 General . 17
4.2.2 RPF for system operations . 18
4.2.3 RPF for power trading . 18
4.2.4 RPF for operations and maintenance . 19
4.3 Methods for forecasting renewable power . 19
4.3.1 General . 19
4.3.2 Classification of forecasting methods . 19
4.3.3 Classification based on time scale . 21
4.3.4 Classification based on spatial range . 22
4.3.5 Classification based on the forecasting model . 22
4.3.6 Classification based on the forecasting form . 24
4.4 Summary . 25
5 NWP technology . 25
5.1 General . 25
5.2 Concept and characteristics of NWP . 25
5.3 Influence on RPF accuracy . 27
5.3.1 Sensitivity analysis . 27
5.3.2 Error source analysis . 28
5.4 Technology progress for improving NWP . 29
5.4.1 General . 29
5.4.2 Global model . 29
5.4.3 Regional model . 31
5.5 Key techniques for improving the forecast accuracy of regional models . 31
5.5.1 Improve the accuracy of the initial conditions . 31
5.5.2 Ensemble prediction systems. 32
5.5.3 Establish regional customized forecasting model . 38
5.5.4 NWP post-processing . 39
5.6 Summary . 39
6 Statistical methods . 39
6.1 General . 39
6.2 Methods . 40
6.3 Applications . 42
6.3.1 General . 42

6.3.2 Time series models . 42
6.3.3 Model output statistics (MOS) . 47
6.3.4 Ensemble composite models (ECM) . 51
6.3.5 Power output models . 53
7 Wind power forecasting (WPF) technology. 54
7.1 General . 54
7.2 Short-term WPF . 54
7.2.1 Relationship between wind power output and meteorological elements . 54
7.2.2 Framework of short-term WPF . 57
7.2.3 Short-term WPF methods . 58
7.3 Ultra-short-term WPF . 62
7.4 Probabilistic WPF . 65
7.4.1 General . 65
7.4.2 Basic concepts and model framework definition . 65
7.4.3 Uncertainty modeling approaches . 66
7.4.4 Probabilistic WPF model . 67
7.5 Wind power ramp event forecasting . 71
7.5.1 General . 71
7.5.2 Quantitative description of wind power ramp events . 71
7.5.3 Forecasting methods of wind power ramp events . 74
7.6 WPF for wind farm clusters . 75
7.6.1 General . 75
7.6.2 Basic concepts of WPF for wind farm clusters. 75
7.6.3 Overall framework of the WPF for wind farm clusters . 76
7.6.4 Physical hierarchy of WPF for wind farm clusters . 78
7.6.5 WPF methods of wind farm clusters . 79
7.7 Other WPF techniques . 82
7.7.1 Medium-term and long-term WPF . 82
7.7.2 WPF for offshore wind farms . 82
7.8 Summary . 83
8 PV power forecasting technology . 83
8.1 General . 83
8.2 Short-term PVPF . 83
8.2.1 General . 83
8.2.2 Meteorological influence factors of PV power generation . 83
8.2.3 Basic concepts for short-term PVPF . 86
8.2.4 Short-term PVPF model . 87
8.2.5 Trends in PVPF development and key technical issues . 89
8.3 Ultra-short-term PVPF. 89
8.3.1 General . 89
8.3.2 Basic concepts for ultra-short-term PVPF . 90
8.3.3 Ultra-short-term PVPF models . 90
8.3.4 Trends in development and key technical issues . 92
8.4 Minute-time-scale PVPF. 92
8.4.1 Basic concepts for minute-time-scale solar power forecasting . 93
8.4.2 Technique routine of minute-time-scale solar power forecasting . 93
8.4.3 Trends in development and key technical issues . 94
8.5 Probabilistic PVPF . 95
8.5.1 Basic concepts of PV power probabilistic forecasting . 95

– 4 – IEC TR 63043:2020  IEC 2020
8.5.2 Probabilistic PVPF model . 96
8.5.3 Trends in development and key technical issues . 98
8.6 Distributed PVPF . 98
8.6.1 General . 98
8.6.2 Basic concepts for distributed PVPF . 99
8.6.3 Distributed PVPF methods . 99
8.6.4 Trends in development and key technical issues . 102
8.7 Summary . 102
9 Renewable energy power forecasting (RPF) evaluation . 103
9.1 General . 103
9.2 Deterministic forecasts of continuous variables . 104
9.2.1 General . 104
9.2.2 Metrics . 104
9.2.3 Mean bias error . 104
9.2.4 Mean absolute error. 105
9.2.5 Root mean square error . 105
9.2.6 Skill score . 106
9.2.7 Correlation coefficient . 106
9.2.8 Maximum prediction error . 107
9.2.9 Pass rate . 107
9.2.10 95 % QDR . 108
9.2.11 Customized metrics . 109
9.3 Deterministic forecasts of categorical (event) variables . 109
9.3.1 General . 109
9.3.2 Occurrence/non-occurrence metrics . 110
9.3.3 Frequency bias . 110
9.3.4 Probability of detection . 110
9.3.5 False alarm ratio . 111
9.3.6 Critical success index . 111
9.3.7 Equitable threat score . 111
9.3.8 Heidke skill score . 111
9.4 Probabilistic forecasts of categorical (event) variables . 112
9.4.1 General . 112
9.4.2 Overall performance . 112
9.4.3 Reliability. 116
9.4.4 Resolution . 117
9.5 Probabilistic forecasts of continuous variables . 118
9.5.1 General . 118
9.5.2 Overall performance . 118
9.5.3 Reliability. 119
9.5.4 Resolution . 119
9.6 Sources of forecast error . 119
9.7 Comparison of forecast performance. 120
9.8 Selection of an optimal forecast solution . 122
10 Conclusions and recommendations. 123
Bibliography . 126

Figure 1 – Forecasting of PV power at different spatial and temporal scales . 21

Figure 2 – Introduced data for PV power forecasting at different spatial and temporal
scales . 21
Figure 3 – Typical process for running a regional model . 26
Figure 4 – Power curve of typical wind turbines . 27
Figure 5 – Characteristics of three kinds of forecasting errors . 28
Figure 6 – Evolution of ECMWF's forecasting skills for the 500 hPa potential height
[35], [54] . 30
Figure 7 – Ensemble forecasting sketch [54] . 33
Figure 8 – Illustration of parameterization schemes for sub-grid physical processes [54] . 38
Figure 9 – MAE (% of capacity) versus look-ahead time for 0 h to3 h forecasts of the
15 min average wind power production from the TWRA aggregate over the one-year
period from October 2015 to September 2016 for each of 5 source-dependent sets of
predictors employed in the predictor source category experiment [96] . 44
Figure 10 – Percentage MAE reduction over persistence by look-ahead time achieved
by each source-dependent set of predictors for 0 h to 3 h forecasts of the 15 min
average TWRA aggregate (capacity of 2 319 MW) power production over the one-year
period from October 2015 to September 2016 [96] . 45
Figure 11 – Percentage MAE reduction by look-ahead time achieved by building
forecasting models with the XGBoost method versus MLR for the “Add existing
external data” (set #4) and “Add targeted sensors” (set #5) predictor sets for 0 h to 3 h
forecasts of the 15 min average TWRA aggregate (capacity of 2 319 MW) power
production over the one year period from October 2015 to September 2016 [96] . 46
Figure 12 – Percentage MAE reduction by look-ahead time achieved by using the “rate
of change” (indirect forecasting) versus “the 15 min average power generation” (direct
forecasting) as the target predictand for the XGBoost model for 0 h to 3 h forecasts of
the 15 min average TWRA aggregate (capacity of 2 319 MW) power production over
the one year period from October 2015 to September 2016 [96] . 47
Figure 13 – Mean absolute error (MAE) in m/s of two 0 h to 18 h NWP-MOS forecasts
of the maximum wind gust in a 15 min period for 33 sites over a 32-case sample of
high wind events as a function of training sample size . 48
Figure 14 – Percentage reduction in the mean absolute error of NWP-based 0 h to 15 h
wind power forecasts for the Tehachapi Wind Resource Area (TWRA) over a one-year
period resulting from the application of 26 statistical forecasting methods to the output
from the United States National Weather Service’s High Resolution Rapid Refresh
(HRRR) model [96] . 49
Figure 15 – Percentage reduction in the mean absolute error (MAE) of wind power
forecasts relative to a baseline of a raw NWP forecast for three NWP models when a
MOS procedure is applied to the NWP output (larger percentages are better) . 51
Figure 16 – Input and output parameters of the three-days-ahead WPF . 54
Figure 17 – Wind power output at different wind speeds under air density of 1,225
kg/m (a typical 2 MW wind turbine) . 55
Figure 18 – EC distribution of a wind farm at different wind speeds and directions . 56
Figure 19 – Wind speed and wind power curves of wind turbines at different air

densities . 57
Figure 20 – Typical framework of short-term WPF . 58
Figure 21 – Principle of short-term WPF based on physical approaches . 59
Figure 22 – Flowchart of short-term WPF based on statistical approaches . 60
Figure 23 – Short-term WPF model based on ANN . 60
Figure 24 – Input and output parameters of the 4 h ultra-short-term WPF . 62
Figure 25 – Flowchart of ultra-short-term WPF . 63
Figure 26 – Generalized combination methods of ultra-short-term WPF . 64

– 6 – IEC TR 63043:2020  IEC 2020
Figure 27 – Methods used for probabilistic forecasting . 65
Figure 28 – Overview of probabilistic wind power forecasting . 66
Figure 29 – Wind power probability distribution forecasting results . 67
Figure 30 – Filtering approach with ensemble NWP as input . 68
Figure 31 – Dimension reduction approach with ensemble NWP as input . 69
Figure 32 – Direct approach with ensemble NWP as input . 69
Figure 33 – Two ramp events of a wind farm . 72
Figure 34 – Overall framework of the WPF system for wind farm clusters . 77
Figure 35 – Physical levels of WPF for wind farm clusters . 78
Figure 36 – Flow chart of the accumulation method . 79
Figure 37 – Flow chart of the statistical upscaling method. 80
Figure 38 – Flow chart of the space resource matching method . 81
Figure 39 – Volt-ampere characteristic curve of PV modules corresponding to different
irradiance . 84
Figure 40 – Volt-ampere characteristics of PV modules at different temperatures . 85
Figure 41 – Short-term forecasting models of PV power generation . 87
Figure 42 – PV short-term power physical forecasting method technical route . 89
Figure 43 – Basic technology roadmap for pv power ultra-short-term forecasting . 91
Figure 44 – Ultra-short-term PVPF based on machine learning model . 91
Figure 45 – Minute-time-scale solar power forecasting technique process . 94
Figure 46 – Example of probabilistic PV model . 96
Figure 47 – Forecasting process of physical PV power probabilistic forecasting model . 96
Figure 48 – Forecasting process of statistical probabilistic PVPF model . 97
Figure 49 – Framework of clustering statistical forecasting method for distributed PVPF. 100
Figure 50 – Framework of grid forecasting method for distributed PVPF . 101
Figure 51 – Comparison between the forecasting results of the clustering statistical
method and the grid forecast method . 102
Figure 52 – Example of a reliability diagram for two probabilistic forecasts (Forecast A
and Forecast B) of a binary event . 117

Table 1 – Classification of RPF methods . 19
Table 2 – Features of global NWP models . 30
Table 3 – Comparison of different ensemble prediction methodologies and their
attributes [46], [73] . 37
Table 4 – Output modes of probabilistic forecasting . 67
Table 5 – Advantages and disadvantages of ramp events definitions . 73
Table 6 – Data sources of WPF for wind farm clusters . 77
Table 7 – Comparison of WPF methods for wind farm clusters. . 81
Table 8 – Contingency table for forecasts of the occurrence/non-occurrence of an

event . 110
Table 9 – A summary of recommended metrics for frequently used forecast types . 121

INTERNATIONAL ELECTROTECHNICAL COMMISSION
____________
RENEWABLE ENERGY POWER FORECASTING TECHNOLOGY

FOREWORD
1) The International Electrotechnical Commission (IEC) is a worldwide organization for standardization comprising
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8) Attention is drawn to the Normative references cited in this publication. Use of the referenced publications is
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rights. IEC shall not be held responsible for identifying any or all such patent rights.
IEC 63043 has been prepared by subcommittee 8A: Grid Integration of Renewable Energy
Generation, of IEC technical committee 8: Systems aspects of electrical energy supply. It is a
Technical Report.
The text of this Technical Report is based on the following documents:
Draft Report on voting
8A/71/DTR 8A/73/RVDTR
Full information on the voting for its approval can be found in the report on voting indicated in
the above table.
The language used for the development of this Technical Report is English.
This document was drafted in accordance with ISO/IEC Directives, Part 2, and developed in
accordance with ISO/IEC Directives, Part 1 and ISO/IEC Directives, IEC Supplement, available
at www.iec.ch/members_experts/refdocs. The main document types developed by IEC are
described in greater detail at www.iec.ch/standardsdev/publications.

– 8 – IEC TR 63043:2020  IEC 2020
The committee has decided that the contents of this document will remain unchanged until the
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INTRODUCTION
The purpose of this IEC Technical Report (TR) is to describe common practices and the state
of the art for renewable energy power forecasting, which includes general data requirements,
methods for renewable energy power forecasting and forecast error evaluation.
Various stakeholders, including transmission system operators, transmission system owners,
utilities, renewable energy generation plant developers, academic units, research institutions,
certifying bodies and standardization groups, require a common understanding of renewable
energy power forecasting methods, data and evaluation techniques so they can incorporate
them in their operations.
Renewable energy power forecasting finds a broad application in many areas of electrical
engineering related to design, analysis, market trading, and optimisation of the power system.
Among others, forecasting could be as an input to the operation and management of the
renewable energy generation plants and can improve the economic efficiency and reliability of
the power system.
Renewable energy power forecasting is increasingly important in multi-stakeholder systems
where renewable plant manufacturers, renewable energy generation plant developers and
operators, as well as the power system operators, need to have a common understanding about
the capabilities and methods associated with renewable energy power forecasting.

– 10 – IEC TR 63043:2020  IEC 2020
RENEWABLE ENERGY POWER FORECASTING TECHNOLOGY

1 Scope
This Technical Report, which is informative in its nature, describes common practices and state
of the art for renewable energy power forecasting technology, including general data demands,
renewable energy power forecasting methods and forecasting error evaluation. For the
purposes of this document, renewable energy refers to variable renewable energy, which mainly
comprises wind power and photovoltaic (PV) power – these are the focus of the document.
Other variable renewable energies, like concentrating solar power, wave power and tidal power,
etc., are not presented in this document, since their capacity is small, while hydro power
forecasting is a significantly different field, and so not covered here.
The objects of renewable energy power forecasting can be wind turbines, or a wind farm, or a
region with lots of wind farms (respectively PV systems, PV power stations and regions with
high PV penetration). This document focuses on providing technical guidance concerning
forecasting technologies of multiple spatial and temporal scales, probabilistic forecasting, and
ramp event forecasting for wind power and PV power.
This document outlines the basic aspects of renewable energy power forecasting technology.
This is the first IEC document related to renewable energy power forecasting. The contents of
this document will find an application in the following potential areas:
• support the development and future research for renewable energy power forecasting
technology, by showing current state of the art;
• evaluation of the forecasting performance during the design and operation of renewable
energy power forecasting system;
• provide information for benchmarking renewable forecasting technologies, including
methods used, data required and evaluation techniques.
2 Normative references
The following documents are referred to in the text in such a way that some or all of their content
constitutes requirements of this document. For dated references, only the edition cited applies.
For undated references, the latest edition of the referenced document (including any
amendments) applies.
IEC 61400-12-2, Wind turbines – Part 12-2: Power performance of electricity-producing wind
turbines based on nacelle anemometry
3 Terms, definitions and abbreviated terms
For the purposes of this document, the following terms and definitions apply.
ISO and IEC maintain terminological databases for use in standardization at the following
addresses:
• IEC Electropedia: available at http://www.electropedia.org/
• ISO Online browsing platform: available at http://www.iso.org/obp

3.1 Terms and definitions
3.1.1
accumulation method
wind power forecasting method for wind farm clusters that directly accumulates the forecasting
results of each wind farm output
3.1.2
combination approaches, pl.
approaches usually used to describe the forecasting models which combine physical
approaches with statistical approaches
3.1.3
data assimilation
assimilation combining recent observation data with the background (prior) forecast in real time,
to adjust forecast model trajectories and update the background field
Note 1 to entry: In numerical weather prediction (NWP), this helps the weather forecast become more accurate.
3.1.4
day ahead power forecasting
timescale used in forecasting, which is from the current time to the next 24 hours to 72 hours
3.1.5
days to week ahead power forecasting
timescale used in forecasting, which is from the day ahead to the week ahead
3.1.6
deterministic forecasting
kind of forecasting method that can output deterministic results
3.1.7
deterministic forecasting evaluation
evaluation of the specific forecasting value at a certain moment in the future
3.1.8
distributed PV power forecasting
a method to forecast the total power output of distributed PVs in a certain region
3.1.9
ensemble forecasting
NWP method which produces a set of forecasts instead of making a single forecast
Note 1 to entry: This set of forecasts aims to give an indication of the range of possible future states of the
atmosphere.
3.1.10
event forecasting evaluation
evaluation of the forecasting results of specific events
Note 1 to entry: For example wind or solar ramp events.
3.1.11
horizon
length of the forecast look-ahead time

– 12 – IEC TR 63043:2020  IEC 2020
3.1.12
minutes and hours ahead power forecasting
type of forecasting which is in minute scale (up to 15 min) or ultra-short-term (15 min to 6 h or
8 h)
3.1.13
nonparametric modelling
distribution-free without any assumptions of the distribution type
Note 1 to entry: It directly calculates the quantile or distribution function of the unknown random variable by means
of data analysis methods.
3.1.14
numerical weather prediction
NWP
method to predict weather which numerically solves the basic equations of atmospheric motion
based on the most recent observations that best represent the current atmospheric conditions
3.1.15
parameterization schemes
methods to capture the quantitative physical characteristics of radiative, convective and
diffusive processes in the atmosphere and at the interface between the atmosphere and the
surface.
Note 1 to entry: These processes are often determined by relatively small spatial scales, and are used in NWP
models.
3.1.16
parametric modelling
model to use a predetermined distribution type describing the probability density function (PDF)
of the unknown random variable
3.1.17
persistence forecasting
a method to use the measured power value at the current moment as the forecasted power at
the future time
3.1.18
physical approaches
mathematical and physical models which are used to describe the physical factors
3.1.19
power forecasting of renewable plant clusters
the forecasting of the overall output of wind or solar PV clusters
3.1.20
probabilistic forecasting
a kind of forecasting methods that focuses on the uncertainty of power output
Note 1 to entry: Including wind power probabilistic forecasting and PV power probabilistic forecasting. The
forecasting results could be the PDF, cumulative distribution function (CDF) of the random variable of power or the
prediction intervals at certain probability levels.
3.1.21
probabilistic forecasting evaluation
evaluation of the forecasting results of the uncertainty of power output

3.1.22
ramp events, pl.
significant changes of power output in a short period
Note 1 to entry: These may refer specifically to those events not caused by the expected change due to the expected
change in output of solar PV. Such events are prone to cause frequency fluctuation and power quality deterioration,
potentially impacting the reliable operation of the power grid.
3.1.23
ramp magnitude
variation of power output in the observation period
3.1.24
ramp rate
variation rate of wind power in the observation period
3.1.25
resolution
spatial or temporal scales at which forecasts are made, measured in kilometers (spatial) or
minutes/hours
3.1.26
statistical approach
mathematical model which is used to describe the relationship between historical NWP data,
weather data and historical power output of a wind farm or a PV power station
3.1.27
statistical upscaling method
establishment of an upscaling model with part of a set of points to estimate the total
Note 1 to entry: It is the total regional power output estimated from a subset of wind farms or PV stations.
3.1.28
stochastic process for forecast development
stochastic process/model to incorporate random variation
Note 1 to entry: Usually based on fluctuations observed in historical data for a selected period using standard time-
series techniques. The use of solely historical data is the main difference compared to probabilistic processes/models,
which apply variations generated from some type of perturbation of the prediction process.
3.1.29
wake effect
phenomenon of wind speed decreasing after wind turbines extract power from the wind
3.2 Abbreviated terms
AE analog ensemble
ANN artificial neural network
AR autoregressive
ARMA autoregressive moving average
BPNN back propagation neural network
BS Brier score
BSS Brier skill score
CART classification and regression tree
CDF cumulative distribution function
CFD computational fluid dynamics

– 14 – IEC TR
...