scholarly journals Probabilistic forecasting of wind power production losses in cold climates: a case study

2018 ◽  
Vol 3 (2) ◽  
pp. 667-680 ◽  
Author(s):  
Jennie Molinder ◽  
Heiner Körnich ◽  
Esbjörn Olsson ◽  
Hans Bergström ◽  
Anna Sjöblom
Keyword(s):  
Wind Power ◽  
Energy Production ◽  
Spatial Representation ◽  
Forecast Skill ◽  
Cold Climates ◽  
Probabilistic Forecasting ◽  
Probabilistic Forecasts ◽  
Forecasting System ◽  
Production Losses

Abstract. The problem of icing on wind turbines in cold climates is addressed using probabilistic forecasting to improve next-day forecasts of icing and related production losses. A case study of probabilistic forecasts was generated for a 2-week period. Uncertainties in initial and boundary conditions are represented with an ensemble forecasting system, while uncertainties in the spatial representation are included with a neighbourhood method. Using probabilistic forecasting instead of one single forecast was shown to improve the forecast skill of the ice-related production loss forecasts and hence the icing forecasts. The spread of the multiple forecasts can be used as an estimate of the forecast uncertainty and of the likelihood for icing and severe production losses. Best results, both in terms of forecast skill and forecasted uncertainty, were achieved using both the ensemble forecast and the neighbourhood method combined. This demonstrates that the application of probabilistic forecasting for wind power in cold climates can be valuable when planning next-day energy production, in the usage of de-icing systems and for site safety.

scholarly journals Probabilistic forecasting of wind power production losses in cold climates: A case study

2017 ◽  
Author(s):  
Jennie P. Söderman ◽  
Heiner Körnich ◽  
Esbjörn Olsson ◽  
Hans Bergström ◽  
Anna Sjöblom
Keyword(s):  
Wind Power ◽  
Energy Production ◽  
Cold Climate ◽  
Forecast Skill ◽  
Cold Climates ◽  
Probabilistic Forecasting ◽  
Probabilistic Forecasts ◽  
Forecasting System ◽  
Production Losses

Abstract. The problem of icing on wind turbines in cold climates is addressed using probabilistic forecasting to improve next- day forecasts of icing and related production losses. A case study of probabilistic forecasts was generated for a two- week period. Uncertainties in initial and boundary conditions are represented with an ensemble forecasting system, while uncertainties in the spatial representation are included with a neighbourhood method. Using probabilistic forecasting instead of one single forecast was shown to improve the forecast skill of the ice-related production loss forecasts and hence the icing forecasts. The spread of the multiple forecasts can be used as an estimate of the forecast uncertainty and of the likelihood for icing and severe production losses. Best results, both in terms of forecast skill and forecasted uncertainty, were achieved using both the ensemble forecast and the neighbourhood method combined. This demonstrates that the application of probabilistic forecasting for wind power in cold climate can be valuable when planning next-day energy production, in the usage of de-icing systems, and for site safety.

Tracking bits of information through forecasting systems: from source to decision

2020 ◽  
Author(s):  
Steven Weijs ◽  
Hossein Foroozand
Keyword(s):  
Information Theory ◽  
Monitoring Network ◽  
Sources Of Information ◽  
Probabilistic Forecasting ◽  
Good Decision ◽  
Monitoring Network Design ◽  
Information Monitoring ◽  
Maximum Value ◽  
Probabilistic Forecasts ◽  
Forecasting System

<p>Probabilistic forecasts are essential for good decision making, because they communicate the forecaster's best attempt at representation of both information available and the remaining uncertainty of a variable of interest. The amount of information provided, which can be measured in bits using information theory, would then be a natural measure of success for the forecast in a verification exercise. On the other hand, it may seem rational to tune the forecasting system to provide maximum value to users. Somewhat counter-intuitively, there are arguments against tuning for maximum value. When the design of the forecasting system also includes the choice of the sources of information, monitoring network optimization becomes part of the problem to solve.  <br>In this presentation, we give a brief overview of the different roles information theory can have in these different aspects of probabilistic forecasting. These roles range from analysis of predictability, model selection, forecast verification, monitoring network design, and data assimilation by ensemble weighting. Using the same theoretical framework for all these aspects has the advantage that some connections can be made that may eventually lead to a more unified perspective on forecasting. </p>

scholarly journals A methodology for optimizing probabilistic wind power forecasting

2018 ◽  
Vol 45 ◽  
pp. 289-294
Author(s):  
Christos Stathopoulos ◽  
George Galanis ◽  
Nikolaos S. Bartsotas ◽  
George Kallos
Keyword(s):  
Wind Power ◽  
Reference Method ◽  
Wind Farms ◽  
Atmospheric Model ◽  
Optimization Techniques ◽  
Probabilistic Forecasting ◽  
Time Data ◽  
Confidence Levels ◽  
Probabilistic Forecasts ◽  
Error Distributions

Abstract. Deterministic wind power forecasts enclose an inherent uncertainty due to several sources of error. In order to counterbalance this deficiency, an analysis of the error characteristics and construction of probabilistic forecasts with associated confidence levels is necessary for the quantification of the corresponding uncertainty. This work proposes a probabilistic forecasting method using an atmospheric model, optimization techniques for addressing the temporal error dependencies and Kalman filtering for eliminating systematic errors and enhancing the symmetry-normality of the shaped error distributions. The method is applied in case studies, using real time data from four wind farms in Greece. The performance is compared against a reference method as well as other common methods showing an improvement in the predictive reliability.

scholarly journals Using quantile regression to extend an existing wind power forecasting system with probabilistic forecasts

Wind Energy ◽  
2006 ◽  
Vol 9 (1-2) ◽  
pp. 95-108 ◽  
Author(s):  
Henrik Aalborg Nielsen ◽  
Henrik Madsen ◽  
Torben Skov Nielsen
Keyword(s):  
Quantile Regression ◽  
Wind Power ◽  
Wind Power Forecasting ◽  
Probabilistic Forecasts ◽  
Forecasting System ◽  
Power Forecasting

scholarly journals Probabilistic reference model for hourly PV power generation forecasting

2020 ◽  
Vol 152 ◽  
pp. 01002
Author(s):  
L. Alfredo Fernandez-Jimenez ◽  
Sonia Terreros-Olarte ◽  
Alberto Falces ◽  
Pedro M. Lara-Santillan ◽  
Enrique Zorzano-Alba ◽  
...  
Keyword(s):  
Time Series ◽  
Reference Model ◽  
Power Production ◽  
Forecasting Model ◽  
Probabilistic Forecasting ◽  
Mean Power ◽  
Forecasting Models ◽  
Proposed Model ◽  
Probabilistic Forecasts

This paper presents a new probabilistic forecasting model of the hourly mean power production in a Photovoltaic (PV) plant. It uses the minimal information and it can provide probabilistic forecasts in the form of quantiles for the desired horizon, which ranges from the next hours to any day in the future. The proposed model only needs a time series of hourly mean power production in the PV plant, and it is intended to fill a gap in international literature where hardly any model has been proposed as a reference for comparison or benchmarking purposes with other probabilistic forecasting models. The performance of the proposed forecasting model is tested, in a case study, with the time series of hourly mean power production in a PV plant with 1.9 MW capacity. The results show an improvement with respect to the reference probabilistic PV power forecasting models reported in the literature.

scholarly journals An open challenge to advance probabilistic forecasting for dengue epidemics

2019 ◽  
Vol 116 (48) ◽  
pp. 24268-24274 ◽  
Author(s):  
Michael A. Johansson ◽  
Karyn M. Apfeldorf ◽  
Scott Dobson ◽  
Jason Devita ◽  
Anna L. Buczak ◽  
...  
Keyword(s):  
Public Health ◽  
Infectious Disease ◽  
Situational Awareness ◽  
Public Health Problem ◽  
Forecast Skill ◽  
Health Needs ◽  
Global Public Health ◽  
Probabilistic Forecasting ◽  
Probabilistic Forecasts ◽  
Wide Range

A wide range of research has promised new tools for forecasting infectious disease dynamics, but little of that research is currently being applied in practice, because tools do not address key public health needs, do not produce probabilistic forecasts, have not been evaluated on external data, or do not provide sufficient forecast skill to be useful. We developed an open collaborative forecasting challenge to assess probabilistic forecasts for seasonal epidemics of dengue, a major global public health problem. Sixteen teams used a variety of methods and data to generate forecasts for 3 epidemiological targets (peak incidence, the week of the peak, and total incidence) over 8 dengue seasons in Iquitos, Peru and San Juan, Puerto Rico. Forecast skill was highly variable across teams and targets. While numerous forecasts showed high skill for midseason situational awareness, early season skill was low, and skill was generally lowest for high incidence seasons, those for which forecasts would be most valuable. A comparison of modeling approaches revealed that average forecast skill was lower for models including biologically meaningful data and mechanisms and that both multimodel and multiteam ensemble forecasts consistently outperformed individual model forecasts. Leveraging these insights, data, and the forecasting framework will be critical to improve forecast skill and the application of forecasts in real time for epidemic preparedness and response. Moreover, key components of this project—integration with public health needs, a common forecasting framework, shared and standardized data, and open participation—can help advance infectious disease forecasting beyond dengue.

scholarly journals Probabilistic Forecasting of Wind Turbine Icing Related Production Losses Using Quantile Regression Forests

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 158
Author(s):  
Jennie Molinder ◽  
Sebastian Scher ◽  
Erik Nilsson ◽  
Heiner Körnich ◽  
Hans Bergström ◽  
...  
Keyword(s):  
Machine Learning ◽  
Wind Energy ◽  
Quantile Regression ◽  
Weather Prediction ◽  
Machine Learning Method ◽  
Learning Method ◽  
Cold Climates ◽  
Probabilistic Forecasting ◽  
Probabilistic Forecasts ◽  
Probabilistic Machine Learning

A probabilistic machine learning method is applied to icing related production loss forecasts for wind energy in cold climates. The employed method, called quantile regression forests, is based on the random forest regression algorithm. Based on the performed tests on data from four Swedish wind parks available for two winter seasons, it has been shown to produce valuable probabilistic forecasts. Even with the limited amount of training and test data that were used in the study, the estimated forecast uncertainty adds more value to the forecast when compared to a deterministic forecast and a previously published probabilistic forecast method. It is also shown that the output from a physical icing model provides useful information to the machine learning method, as its usage results in an increased forecast skill when compared to only using Numerical Weather Prediction data. A potential additional benefit in machine learning for some stations was also found when using information in the training from other stations that are also affected by icing. This increases the amount of data, which is otherwise a challenge when developing forecasting methods for wind energy in cold climates.

Development and Operational Status of Wind Power Forecasting System

2013 ◽  
Vol 133 (4) ◽  
pp. 366-372 ◽  
Author(s):  
Isao Aoki ◽  
Ryoichi Tanikawa ◽  
Nobuyuki Hayasaki ◽  
Mitsuhiro Matsumoto ◽  
Shigero Enomoto
Keyword(s):  
Wind Power ◽  
Wind Power Forecasting ◽  
Forecasting System ◽  
Power Forecasting
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