Solar power forecasting

Generation forecasting

The energy generation forecasting problem is closely linked to the problem of weather forecasting . Indeed, this problem is usually divided into two parts, it is one of a number of factors that can be used to estimate the amount of energy in the environment. In general, the way to deal with this problem is usually related to the spatial and temporal scales we are interested in, which yields to different approaches can be found in the literature. In this sense, it is useful to classify these techniques depending on the forecasting horizon, it is possible to distinguish between now-casting (forecasting 3-4 hours ahead),short-term forecasting (up to 7 days ahead) and long-term forecasting(months, years …) Solar radiation follows the physical and biological development of the earth. And spatial and sequential heterogeneity of the influence of the environment and the environment and the environment. Therefore, solar radiation drives place efficiency and plant life allotment, organism a key feature in a dedeveloped and forest sciences that is obligatory to be known precisely. The quantity of solar radiation available at the earth’s surface is at the outset of the world. On the other hand, A complete explanation of its freedom time is also required, as is the casing in mountainous region. Predominantly, limited territory, in the case of a solar radiation, is a precise spatial model of inward bound solar radiation. In the final time, the field of solar radiation has been projected, such as the use of Geographical Information Systems (GIS), an artificial intelligence or post-dispensation of satellite technical stand. Solar radiation can also be evaluated using numerical weather forecast (NWP) models. Nevertheless,

Otherwise, exclamation technique can not be more widely available. Even though their dependability is powerfully necessary on the opening coldness between position, they have a greater dependence on the method. Therefore, while spatial spatial adequacy is accessible, disturbance method is preferred. Conventionally, solar radiation has also been used to increase the temperature of the environment. Though, the number of experimental record which has been reduced to an appropriate technique for solar radiation evaluation. Nevertheless, radiometric stations are frequently encountered in the region of the United States. This theory is particularly applicable to the high spatial impact of solar radiation in these provinces. As an outcome, particular interrupt method that tolerate include external foundation should be used to make clear this extra spatial unpredictability. Several diverse spatial interruptions techniques can be established. On the other hand, data ease of use in the region is often extremely restricted. As a result, it is difficult to construct a precise solar radiation climate change in climate change. typical during basin and flat area, This theory is particularly applicable to the high spatial impact of solar radiation in these provinces. As an outcome, particular interrupt method that tolerate include external foundation should be used to make clear this extra spatial unpredictability. Several diverse spatial interruptions techniques can be established. On the other hand, data ease of use in the region is often extremely restricted. As a result, it is difficult to construct a precise solar radiation climate change in climate change. typical during basin and flat area, This theory is particularly applicable to the high spatial impact of solar radiation in these provinces. As an outcome, particular interrupt method that tolerate include external foundation should be used to make clear this extra spatial unpredictability. Several diverse spatial interruptions techniques can be established. On the other hand, data ease of use in the region is often extremely restricted. As a result, it is difficult to construct a precise solar radiation climate change in climate change. As an outcome, particular interrupt method that tolerate include external foundation should be used to make clear this extra spatial unpredictability. Several diverse spatial interruptions techniques can be established. On the other hand, data ease of use in the region is often extremely restricted. As a result, it is difficult to construct a precise solar radiation climate change in climate change. As an outcome, particular interrupt method that tolerate include external foundation should be used to make clear this extra spatial unpredictability. Several diverse spatial interruptions techniques can be established. On the other hand, data ease of use in the region is often extremely restricted. As a result, it is difficult to construct a precise solar radiation climate change in climate change.

Solar radiation is a variable illustration, with variable temperature and precipitation, in a fraction of the amount required by the radiometric sensors. It is extremely sensitive to the environment. Predominantly, ground surface confronts the traditional method of measurement. Geo-statistics a stochastic approach to determination of the relationship between a prediction and a prediction.

Nowcasting

Nowcasting included the detailed description of the current weather along with forecasts for up to 3-4 hours. This plant is very important for grid operators and can be considered manageable, at least in a certain degree, as solar thermal power plants. Nowcasting services are usually related to very high temporal resolution (a forecast every 10 or 15 minutes). Several approaches can be found in the literature, which are mainly dependent on the following hypotheses:

1. Statistical techniques are usually based on meteorological measurement data, which is used as a data model for the parameters of a model (I. Espino et al., 2011). These techniques include the use of any kind of statistical approach, such as autoregressive moving average (ARMA, ARIMA, …), neural networks, support vector machines , etc. These approaches are usually benchmarked to a persistent approach in order to evaluate their improvements. This persistence approach just assumes that any variable at time step is the value it took in a previous time.
2. Since the launch of Earth observing satellites, such as MSG , nowcasting techniques have also been developed from an image processing point of view. The main advantage of these techniques is the possibility to monitor a lot of meteorological information in almost real time. This paper is based on an estimation of future atmospheric values, as described in Alvarez et al. , 2010.

Solar PV short-term forecasting

Short-term forecasting provides predictions up to 7 days ahead. This kind of forecast is also valuable for grid operators in order to make decisions for grid operations, as well as for electric market operators. ^[1] Under this perspective, the meteorological resources are estimated at a different temporal and spatial resolution. This implies that meteorological variables and phenomena are looked at from a more general perspective, not as local as nowcasting services. In this sense, most of the approaches make use of different numerical weather prediction models (NWP). Currently, several models are available for this purpose, such as Global Forecast System(GFS) or data provided by the European Center for Medium Range Weather Forecasting ( ECMWF ). These two models are considered the state of the art of global forecast models, which provide meteorological forecasts all over the world. In order to increase spatial and temporal resolution of these models, other models have been developed which are generally called mesoscale models. Among others, HIRLAM , WRF or MM5are the most representative of these models since they are widely used by different communities. To get a better understanding of this issue, we need to know how to obtain accurate results. In addition, sophisticated techniques such as data assimilation might be used in order to produce more realistic simulations. Finally, some communities argue for the use of post-processing techniques, ounce the models’ output is obtained, in order to obtain a probabilistic point of view of the accuracy of the output. This is usually done with a set of techniques that are different from those of different models in a given theoretical and quantitative approach, and are proposed by Bacher et al. (2009)

Solar PV long-term forecasting

Long-term forecasting is usually referred to as the annual or monthly availability resource. This is useful for energy producers and to negotiate contracts with financial entities or utilities that distribute the generated energy. In general, these long-term forecasting is usually done at a lower scale than any of the previous two approaches. Hence, most of these models are run with mesoscale models fed with reanalysis data as input and which output is postprocessed with statistical approaches based on measured data.

Energetic models

Any output from any model described above must be converted into a PV plant will produce. This step is usually done with statistical approaches that try to correlate the amount of available resources with the metered power output. The main advantage of these methods is that the meteorological prediction error, which is the main component of the global error, may be reduced by the uncertainty of the prediction. As it was mentioned before and detailed in Heinemann et al.These methods include ARMA models, neural networks, vector support machines, etc. On the other hand, there is also a description of how to describe a power plant converts the meteorological resource into electric energy, as described in Alonso et al. The main advantage of this type of models is that they are fitted, they are really accurate, they are too sensitive to the meteorological prediction error, which is usually amplified by these models. Hybrid models, finally, are a combination of these two models and they seem to be capable of being outperformed individually.

See also

Energy portal

• Energy forecasting

References

• Y. Chu, M. Li and CFM Coimbra (2016) “Sun-Tracking Imaging System for Intra-Hour DNI Forecasts” Renewable Energy (96), Part A, pp. 792-799.
• Luis Martín, Luis F. Zarzalejo, Jesús Polo, Ana Navarro, Marco Cony, Ruth Marchante, Prediction of global solar irradiance, Solar Energy, Solar Energy, Volume 84, Issue 10, October 2010, Pages 1772-1781, ISSN  0038-092X , doi : 10.1016 / j.solener.2010.07.002 .
• Heinemann, D., Lorenz E., Girodo M. Forecasting of solar radiation. Oldenburg University, Institute of Physics, Energy Meteorology Group.
• Alonso, M., Chenlo F. Estimación de la energía generada por a sistema fotovoltaico conectado a red. CIEMAT. Laboratorio de sistemas fotovoltaicos.
• Alvarez, L., Castaño, CA, Martin, J. A computer vision approach for solar radiation nowcasting using MSG images. EMS Annual Meeting Abstracts. Flight. 7, EMS2010-495, 2010. 10th EMS / 8th ECAC.
• Espino, I., Hernandez, M .. Nowcasting of wind speed using support vector regression. Experiments with Time Series from Gran Canaria. Renewable Energy and Power Quality Journal, ISSN  2172-038X , N9, 12 May 2011.
• Bacher, P., Madsen, H., HA Nielsen Online short-term solar power forecasting. Solar Energy. Vol 83, Issue 10, October 2009: 1772-1783.
• Diagne, HM, David, M., Lauret, P., Boland, J. Solar irradiation forecasting: state-of-the-art and proposal for future developments for small-scale insular grids. In Proceedings of the World Renewable Energy Forum 2012 (WREF 2012), Denver, USA, May 2012.

External links

• SEPA – Predicting Solar Power Production

Weather prediction models

• Documentation of HiRLAM at ECMWF
• Description of HiRLAM at KNMI
• GFS

Electricity market

• Power exchange APX (Netherlands)
• System Operator TenneT (Netherlands)
• Nord Pool (Scandinavia)
• Operadora del Mercado Iberico de Energía – Polo Español, SA (Spain)

Solar PV power forecasting providers

• Aeolis (Dutch)
• AleaSolar – AleaSoft (Spain)
• Datameteo (Italy)
• DNV GL short term solar power forecasting
• enercast (Germany) | Solar energy forecasting and Nowcasting (Worldwide)
• EuroWind (Cologne, Germany) | Solar Power Forecasts
• Enfor (Denmark)
• gWISE SOLAR – (Gnarum)
• IrSOLaV | Solar energy forecasting and Nowcasting (Worldwide)
• Irradiance Data (Germany)
• Solar Plant Weather Forecast (Meteologica)
• Meteo4energy (Czech republic)
• Nnergix | Accurate Solar Power Forecasts – Barcelona
• PVCAST | Short-term PV Yield Energy and Solar Radiation forecasting (Worldwide)
• RENES | Renewable Energy and Solar Radiation Forecasting (Worldwide, Free-of-charge)
• Reuniwatt (France)
• Solargis | Solar Energy forecast and nowcast
• Solcast API | Solar power forecast and radiation forecast with global coverage via modern web API. Offers free trial.
• Steadysun | Solar Production Forecasts Specialist (France)
• WPred (Canada)

Solar radiation map

• Solar radiation world map