This document discusses measuring the benefits of seasonal climate forecasts for predicting photovoltaic (PV) power production in Europe. It analyzes the statistical skill of seasonal forecasts compared to observations, as well as how installed PV capacity and variability in solar radiation affect the potential value of forecasts. By considering skill, capacity, and variability together, the authors aim to better evaluate how climate forecasts could help the solar power sector improve decision-making.
FLARECAST - Das Automatische Vorhersagesystem für SonnenflaresFLARECAST
Vortrag zum Europäischen H2020 Forschungsprojekt FLARECAST an der Fachhochschule Nordwestschweiz FHNW: Über die Notwendigkeit, Sonnenflares besser vorhersagen zu können, die Kommunikation mit verschiedenen Nutzergruppen und den Einsatz von Machine Learning für die Automatisierung der Vorhersagen.
by the examples of two European research projects JHelioviewer and FLARECAST. Talk given for a Taiwanese delegation at the University of Applied Sciences FHNW, Switzerland.
Better Hackathon 2020 ETHZ - Comparing Static And Dynamic Effects Of EarthquakesPRBETTER
As part of the final BETTER Hackathon, project partners prepared 4 hackathon exercises. ETHZ organised this exercise as the challenge promoter for the Geohazards thematic area. This open exercise featured the use of Binder and purposely provided cloud resources but could also be run locally through a Docker image and Docker Compose. The focus of this half-day exercise was to find a convenient way of exploitation of Co-seismic interferograms, by using developed BETTER pipelines. The idea was to produce geocoded maps combining automatically the important results to have a convenient visualisation that helps interpreting results.Participants were expected to be familiar with the Jupyter environment (Python 3) and the most common EO libraries (e.g. GDAL). The recorded part includes the introduction of the exercise in the context of the BETTER project.
FLARECAST - Das Automatische Vorhersagesystem für SonnenflaresFLARECAST
Vortrag zum Europäischen H2020 Forschungsprojekt FLARECAST an der Fachhochschule Nordwestschweiz FHNW: Über die Notwendigkeit, Sonnenflares besser vorhersagen zu können, die Kommunikation mit verschiedenen Nutzergruppen und den Einsatz von Machine Learning für die Automatisierung der Vorhersagen.
by the examples of two European research projects JHelioviewer and FLARECAST. Talk given for a Taiwanese delegation at the University of Applied Sciences FHNW, Switzerland.
Better Hackathon 2020 ETHZ - Comparing Static And Dynamic Effects Of EarthquakesPRBETTER
As part of the final BETTER Hackathon, project partners prepared 4 hackathon exercises. ETHZ organised this exercise as the challenge promoter for the Geohazards thematic area. This open exercise featured the use of Binder and purposely provided cloud resources but could also be run locally through a Docker image and Docker Compose. The focus of this half-day exercise was to find a convenient way of exploitation of Co-seismic interferograms, by using developed BETTER pipelines. The idea was to produce geocoded maps combining automatically the important results to have a convenient visualisation that helps interpreting results.Participants were expected to be familiar with the Jupyter environment (Python 3) and the most common EO libraries (e.g. GDAL). The recorded part includes the introduction of the exercise in the context of the BETTER project.
A New Temperature-Based Model for Estimating Global Solar Radiation in Port-...theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Theoretical work submitted to the Journal should be original in its motivation or modeling structure. Empirical analysis should be based on a theoretical framework and should be capable of replication. It is expected that all materials required for replication (including computer programs and data sets) should be available upon request to the authors.
The International Journal of Engineering & Science would take much care in making your article published without much delay with your kind cooperation.
Learning by Redundancy: how climate multi-model ensembles can help to fight t...matteodefelice
This is my talk for the Severo Ochoa Research Seminar Lecture Series at the Barcelona Supercomputing Center held the 23/09/2015 (http://www.bsc.es/marenostrum-support-services/hpc-education-and-training/severo-ochoa-research-seminar/2309-sors)
The abstract is the following:
Climate Models are sophisticate tools able to simulate the interactions among various components of the Earth system (atmosphere, oceans, bio-sphere, etc.). Those tools are nowadays used for many purposes: to improve the knowledge of our planet, to analyse the projections for the future climate and to forecast the climate at multiple time-scales for a wide range of applications. In the last decade the use of climate ensembles (and multi-model ensembles) has become very common, the dimensionality of climate datasets has increased drastically (thanks also to a general increment of temporal and spatial resolutions of models). Unfortunately, this rise of the dimensionality of datasets did not coincide with the development of techniques designed to cope effectively with this massive amount of information.
A New Temperature-Based Model for Estimating Global Solar Radiation in Port-...theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Theoretical work submitted to the Journal should be original in its motivation or modeling structure. Empirical analysis should be based on a theoretical framework and should be capable of replication. It is expected that all materials required for replication (including computer programs and data sets) should be available upon request to the authors.
The International Journal of Engineering & Science would take much care in making your article published without much delay with your kind cooperation.
Learning by Redundancy: how climate multi-model ensembles can help to fight t...matteodefelice
This is my talk for the Severo Ochoa Research Seminar Lecture Series at the Barcelona Supercomputing Center held the 23/09/2015 (http://www.bsc.es/marenostrum-support-services/hpc-education-and-training/severo-ochoa-research-seminar/2309-sors)
The abstract is the following:
Climate Models are sophisticate tools able to simulate the interactions among various components of the Earth system (atmosphere, oceans, bio-sphere, etc.). Those tools are nowadays used for many purposes: to improve the knowledge of our planet, to analyse the projections for the future climate and to forecast the climate at multiple time-scales for a wide range of applications. In the last decade the use of climate ensembles (and multi-model ensembles) has become very common, the dimensionality of climate datasets has increased drastically (thanks also to a general increment of temporal and spatial resolutions of models). Unfortunately, this rise of the dimensionality of datasets did not coincide with the development of techniques designed to cope effectively with this massive amount of information.
The slides of the talk I gave on April 2011 in Paris at the IEEE Symposium on Computational Intelligence Applications in Smart Grid (http://ieee-ssci.org/2011/ciasg-2011).
Advanced weather forecasting for RES applications: Smart4RES developments tow...Leonardo ENERGY
Recording at: https://youtu.be/45Zpjog95QU
This is the 3rd Smart4RES webinar that will address technological and market challenges in RES prediction and will introduce the Smart4RES strategy to improve weather forecasting models with high resolution.
Through wind and solar applications, Innovative Numerical Weather Prediction and Large-Eddy Simulation approaches will be presented.
Reanalysis Datasets for Solar Resource Assessment Presented at ASES 2014Gwendalyn Bender
Reanalysis datasets are available globally and provide free long-term resource estimates. But are they good enough for solar resource assessments? We explore the accuracy of several datasets compared to current industry standards.
Gensol collected Actual Global Tilted Irradiation (AGTI) of 57 sites from operational projects spread across in India. It was then correlated with Expected Global Tilted Irradiation (EGTI) from the following meteo-databases namely:
1) Meteonorm-7.2
2) SolarGIS,
3) NASA (National Aeronautics and Space Administration),
4) NREL (National Renewable Energy Laboratory)
In our report, we find most representative meteo-data set for each site.
Improving the value of variable and uncertain Power Generation in Energy Syst...CLIC Innovation Ltd
VaGe project’s objective is to improve operational decision making in the power systems when considering the variability and uncertainty of wind, solar, water inflow, heat and electricity demand, their correlations and possible sources of flexibility.
Forecasting long term global solar radiation with an ann algorithmmehmet şahin
and energy-efficient buildings, solar concentrators, photovoltaic-systems and a site-selection of sites for future
power plants). To establish long-term sustainability of solar energy, energy practitioners utilize versatile
predictive models of G as an indispensable decision-making tool. Notwithstanding this, sparsity of solar sites,
instrument maintenance, policy and fiscal issues constraint the availability of model input data that must be
used for forecasting the onsite value of G. To surmount these challenge, low-cost, readily-available satellite
products accessible over large spatial domains can provide viable alternatives. In this paper, the preciseness of
artificial neural network (ANN) for predictive modelling of G is evaluated for regional Queensland, which
employed Moderate Resolution Imaging Spectroradiometer (MODIS) land-surface temperature (LST) as an
effective predictor. To couple an ANN model with satellite-derived variable, the LST data over 2012–2014 are
acquired in seven groups, with three sites per group where the data for first two (2012–2013) are utilised for
model development and the third (2014) group for cross-validation. For monthly horizon, the ANN model is
optimized by trialing 55 neuronal architectures, while for seasonal forecasting, nine neuronal architectures are
trailed with time-lagged LST. ANN coupled with zero lagged LST utilised scaled conjugate gradient algorithm,
and while ANN with time-lagged LST utilised Levenberg-Marquardt algorithm. To ascertain conclusive results,
the objective model is evaluated via multiple linear regression (MLR) and autoregressive integrated moving
average (ARIMA) algorithms. Results showed that an ANN model outperformed MLR and ARIMA models
where an analysis yielded 39% of cumulative errors in smallest magnitude bracket, whereas MLR and ARIMA
produced 15% and 25%. Superiority of an ANN model was demonstrated by site-averaged (monthly) relative
error of 5.85% compared with 10.23% (MLR) and 9.60 (ARIMA) with Willmott's Index of 0.954 (ANN), 0.899
(MLR) and 0.848 (ARIMA). This work ascertains that an ANN model coupled with satellite-derived LST data
can be adopted as a qualified stratagem for the proliferation of solar energy applications in locations that have
an appropriate satellite footprint.
Comparison of Solar Radiation Intensity Forecasting Using ANFIS and Multiple ...journalBEEI
Solar radiation forecasting is important in solar energy power plants (SEPPs) development. The electrical energy generated from the sunlight depends on the weather and climate conditions in the area where the SEPPs are installed. The condition of solar irradiation will indirectly affect the electrical grid system into which the SEPPs are injected, i.e. the amount and direction of the power flow, voltage, frequency, and also the dynamic state of the system. Therefore, the prediction of solar radiation condition is very crucial to identify its impact into the system. There are many methods in determining the prediction of solar radiation, either by mathematical approach or by heuristic approach such as artificial intelligent method. This paper analyzes the comparison of two methods, Adaptive Neuro Fuzzy Inference (ANFIS) method, which belongs into the heuristic methods, and Multiple Linear Regression (MLP) method, which uses a mathematical approach. The performance of both methods is measured using the root mean square error (RMSE) and the mean absolute error (MAE) values. The data of the Swiss Basel city from Meteoblue are used to test the performance of the two methods being compared. The data are divided into four cases, being classified as the training data and the data used as predictions. The solar radiation prediction using the ANFIS method indicates the results which are closer to the real measurement results, being compared to the the use MLP method. The average values of RMSE and MAE achieved are 123.27 W/m2 and 90.91 W/m2 using the ANFIS method, being compared to 138.70 W/m2 and 101.56 W/m2 respectively using the MLP method. The ANFIS method gives better prediction performance of 12.51% for RMSE and 11.71% for MAE with respect to the use of the MLP method.
A Systematic Review of Renewable Energy Trend.pdfssuser793b4e
This paper systematically and successfully reviewed the renewable energy trend from 2010 to 2023. This review
detailed the difference renewable energy and conclusion was drawn that solar photovoltaic (PV) energy has the
leading trend in power generation growth and innovation. This research work explained in detail the most recent
solar photovoltaic optimization techniques and it was observed from the review that hybridization of intelligent and
non-intelligent maximum power point tracking technique has the best tracking power conversion efficiency. The
advantages and disadvantage of solar PV together with the solar optimization and innovational growth trends were
examined. This research showed that clean and renewable energy sources will continue to grow and the solar energy
industry is expected to experience significant growth and rapid innovation in the next 10 years. From the observed
rapid growth and innovation trend in solar energy, the world will have a very cheap, abundant and clean energy
before 2050.
This research aim to forecast solar radiation,how much of electricity can be produced in next four months in two cities of India and performance evaluation of forecasting models. These models have been used for long-term forecasting of solar radiation using time series data.Forecasting models like ARIMA,TBATS have been used for this research.Forecasted solar radiation is further used for forecasting solar electricity generation.Performance evaluation of forecasting models has also been done.
Similar to Measuring the benefits of climate forecasts (20)
Supporting the Energy Union with data & knowledgematteodefelice
Slides presented at the 3rd General Assembly of the H2020 S2S4E project explaining the role of the Joint Research Centre of the European Commission in supporting a fair and effective Energy Union
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
1. Measuring the skill benefits of
climate forecasts in predicting
PV power production
Matteo De Felice, Andrea Alessandri and Maurizio
Pollino
2. Solar Power and Climate
• Today we have plenty of weather/climate datasets of
solar radiation (satellites, reanalyses, NWP, climate
forecasts)
• Here we focus on seasonal predictability of solar
radiation
• The aim of this paper is an assessment of the skills of
seasonal forecasts to predict solar radiation over Europe
• May the information provided by climate forecasts help
the solar power sector to improve their decision-making?
EGU2016-18336 - Climate Services - Underpinning Science Session
5. More information sources
• Skill of seasonal forecasts in predicting PV power
output
• PV Solar Installed capacity
• Solar radiation inter-annual variability
• Using land-cover to mask areas not-suitable for PV
EGU2016-18336 - Climate Services - Underpinning Science Session
8. What is a good forecast?
Allan Murphy in 1993 categorised the “goodness”
of a forecast in…
1 Consistency
Correspondence between forecasts and
judgements
2 Quality
Correspondence between forecasts and
observations
3 Value Incremental benefits of forecasts to users
EGU2016-18336 - Climate Services - Underpinning Science Session
9. “Quality” means “value”?
• A. Murphy underlined that forecasts do not have an
intrinsic value but instead they gain it when they
have a positive influence on on the decisions
made by users of the forecasts.
• Value of a forecast is strictly linked with its quality
but their relationship is rarely linear
EGU2016-18336 - Climate Services - Underpinning Science Session
10. Information layers
Here we assume that the benefit of a climate
forecast of solar power is affected by the following
three factors:
1. Statistical Skill (e.g. BSS): the more the better
2. Installed Capacity: good forecast will have a greater
impact in areas with high installed capacity
3. Inter-annual variability: a forecast can help to cope with the
high variability of solar radiation
EGU2016-18336 - Climate Services - Underpinning Science Session
11. (1/3) Statistical skill
ECMWF System4 vs
Heliosat (SARAH)
1983-2013
Lower tercile
upper part:
DJF - MAM
lower part:
JJA - SON
EGU2016-18336 - Climate Services - Underpinning Science Session
12. (1/3) Statistical skill
ECMWF System4 vs
Heliosat (SARAH)
1983-2013
Upper tercile
upper part:
DJF - MAM
lower part:
JJA - SON
EGU2016-18336 - Climate Services - Underpinning Science Session
13. (1/3) Statistical skill
Modelled PV production
of ECMWF System4 vs
Heliosat (SARAH) +
EOBS
1983-2013
Lower tercile
upper part:
DJF - MAM
lower part:
JJA - SON
EGU2016-18336 - Climate Services - Underpinning Science Session
14. (1/3) Statistical skill
Modelled PV production
of ECMWF System4 vs
Heliosat (SARAH) +
EOBS
1983-2013
Upper tercile
upper part:
DJF - MAM
lower part:
JJA - SON
EGU2016-18336 - Climate Services - Underpinning Science Session
15. PV Suitability
• Map of suitability of PV
derived by the work by
Hansen & Thorn (PV
potential and potential PV
rent in European regions)
• Based on the Corine Land
Cover 2006 (CLC2006)
• Used to mask out grid
points from analysis
EGU2016-18336 - Climate Services - Underpinning Science Session
16. (2/3) Installed Capacity
• PV cumulative installed capacity in 2014 (Data
extrapolated from the Solar-Power Europe Global
Market Outlook)
EGU2016-18336 - Climate Services - Underpinning Science Session
19. Putting things together
A matrix of this type should be designed in
collaboration with the end-user
EGU2016-18336 - Climate Services - Underpinning Science Session
21. Comments
• We should focus not only on skill but on all the
factors influencing the decisions
• When providing a service focus on value and not
(only) on quality
EGU2016-18336 - Climate Services - Underpinning Science Session