Comparison of Two Solar Probabilistic Forecasting Methodologies for Microgrids Energy Efficiency
In this paper, the performances of two approaches for solar probabilistic are evaluated using a set of metrics previously tested by the meteorology verification community. A particular focus is put on several scores and the decomposition of a specific probabilistic metric: the continuous rank probab...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/6/1679 |
| _version_ | 1797540931180691456 |
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| author | Luis Mazorra-Aguiar Philippe Lauret Mathieu David Albert Oliver Gustavo Montero |
| author_facet | Luis Mazorra-Aguiar Philippe Lauret Mathieu David Albert Oliver Gustavo Montero |
| author_sort | Luis Mazorra-Aguiar |
| collection | DOAJ |
| description | In this paper, the performances of two approaches for solar probabilistic are evaluated using a set of metrics previously tested by the meteorology verification community. A particular focus is put on several scores and the decomposition of a specific probabilistic metric: the continuous rank probability score (CRPS) as they give extensive information to compare the forecasting performance of both methodologies. The two solar probabilistic forecasting methodologies are used to produce intra-day solar forecasts with time horizons ranging from 1 h to 6 h. The first methodology is based on two steps. In the first step, we generated a point forecast for each horizon and in a second step, we use quantile regression methods to estimate the prediction intervals. The second methodology directly estimates the prediction intervals of the forecasted clear sky index distribution using past data as inputs. With this second methodology we also propose to add solar geometric angles as inputs. Overall, nine probabilistic forecasting performances are compared at six measurements stations with different climatic conditions. This paper shows a detailed picture of the overall performance of the models and consequently may help in selecting the best methodology. |
| first_indexed | 2024-03-10T13:08:06Z |
| format | Article |
| id | doaj.art-c4bebb05ed62469b900fbb3328c80bb4 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T13:08:06Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-c4bebb05ed62469b900fbb3328c80bb42023-11-21T10:57:46ZengMDPI AGEnergies1996-10732021-03-01146167910.3390/en14061679Comparison of Two Solar Probabilistic Forecasting Methodologies for Microgrids Energy EfficiencyLuis Mazorra-Aguiar0Philippe Lauret1Mathieu David2Albert Oliver3Gustavo Montero4IUSIANI, University of Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, SpainPIMENT, University of La Reunion, Saint-Denis, 97410 Reunion, FrancePIMENT, University of La Reunion, Saint-Denis, 97410 Reunion, FranceIUSIANI, University of Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, SpainIUSIANI, University of Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, SpainIn this paper, the performances of two approaches for solar probabilistic are evaluated using a set of metrics previously tested by the meteorology verification community. A particular focus is put on several scores and the decomposition of a specific probabilistic metric: the continuous rank probability score (CRPS) as they give extensive information to compare the forecasting performance of both methodologies. The two solar probabilistic forecasting methodologies are used to produce intra-day solar forecasts with time horizons ranging from 1 h to 6 h. The first methodology is based on two steps. In the first step, we generated a point forecast for each horizon and in a second step, we use quantile regression methods to estimate the prediction intervals. The second methodology directly estimates the prediction intervals of the forecasted clear sky index distribution using past data as inputs. With this second methodology we also propose to add solar geometric angles as inputs. Overall, nine probabilistic forecasting performances are compared at six measurements stations with different climatic conditions. This paper shows a detailed picture of the overall performance of the models and consequently may help in selecting the best methodology.https://www.mdpi.com/1996-1073/14/6/1679probabilistic solar forecastingquantile regression modelsCRPS |
| spellingShingle | Luis Mazorra-Aguiar Philippe Lauret Mathieu David Albert Oliver Gustavo Montero Comparison of Two Solar Probabilistic Forecasting Methodologies for Microgrids Energy Efficiency Energies probabilistic solar forecasting quantile regression models CRPS |
| title | Comparison of Two Solar Probabilistic Forecasting Methodologies for Microgrids Energy Efficiency |
| title_full | Comparison of Two Solar Probabilistic Forecasting Methodologies for Microgrids Energy Efficiency |
| title_fullStr | Comparison of Two Solar Probabilistic Forecasting Methodologies for Microgrids Energy Efficiency |
| title_full_unstemmed | Comparison of Two Solar Probabilistic Forecasting Methodologies for Microgrids Energy Efficiency |
| title_short | Comparison of Two Solar Probabilistic Forecasting Methodologies for Microgrids Energy Efficiency |
| title_sort | comparison of two solar probabilistic forecasting methodologies for microgrids energy efficiency |
| topic | probabilistic solar forecasting quantile regression models CRPS |
| url | https://www.mdpi.com/1996-1073/14/6/1679 |
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