DOI: 10.1049/iet-smt.2013.0135. Photovoltaic power forecasting using statistical methods: impact of weather data

Photovoltaic power forecasting using statistical methods: impact of weather data

10.1049/iet-smt.2013.0135

Crossref journal-article

Institution of Engineering and Technology (IET)

IET Science, Measurement & Technology (265)

Abstract

An important issue for the growth and management of grid‐connected photovoltaic (PV) systems is the possibility to forecast the power output over different horizons. In this work, statistical methods based on multiregression analysis and the Elmann artificial neural network (ANN) have been developed in order to predict power production of a 960 kWP grid‐connected PV plant installed in Italy. Different combinations of the time series of produced PV power and measured meteorological variables were used as inputs of the ANN. Several statistical error measures are evaluated to estimate the accuracy of the forecasting methods. A decomposition of the standard deviation error has been carried out to identify the amplitude and phase error. The skewness and kurtosis parameters allow a detailed analysis of the distribution error.

Bibliography

De Giorgi, M. G., Congedo, P. M., & Malvoni, M. (2014). Photovoltaic power forecasting using statistical methods: impact of weather data. IET Science, Measurement & Technology, 8(3), 90â97. Portico.

Authors 3

Maria Grazia De Giorgi (first)
Paolo Maria Congedo (additional)
Maria Malvoni (additional)

References 29 Referenced 220

GSE (Italian manager of energy services). Rapporto statistico; 2011. 〈http://www.gse.it〉
10.1016/j.renene.2006.06.014
10.1016/j.solener.2008.02.003
10.1016/j.renene.2007.09.020
10.1016/j.solener.2010.02.006
10.1016/j.solener.2010.07.002
10.1016/j.energy.2010.10.032
{'key': 'e_1_2_8_9_1', 'first-page': '773', 'volume-title': 'Forecasting power output for grid‐connected photovoltaic power system without using solar radiation measurement', 'author': 'Cai T.', 'year': '2010'} / Forecasting power output for grid‐connected photovoltaic power system without using solar radiation measurement by Cai T. (2010)
10.1016/j.solener.2010.08.011
10.1109/JSTARS.2009.2020300
10.1016/j.renene.2012.01.108
{'key': 'e_1_2_8_13_1', 'first-page': '3659', 'article-title': 'Application of recurrent neural network to short‐term‐ahead generating power forecasting for photovoltaic system', 'volume': '1', 'author': 'Yona A.', 'year': '2007', 'journal-title': 'IEEE Power Eng. Soc. Gen. Meet.'} / IEEE Power Eng. Soc. Gen. Meet. / Application of recurrent neural network to short‐term‐ahead generating power forecasting for photovoltaic system by Yona A. (2007)
10.1016/j.enconman.2009.11.024
10.1016/j.renene.2006.12.017
10.1016/j.solener.2009.05.016
10.1016/j.enconman.2007.12.030
{'volume-title': 'Application of neural network to 24‐hour‐ahead generating power forecasting for PV system', 'year': '2008', 'author': 'Yona A.', 'key': 'e_1_2_8_18_1'} / Application of neural network to 24‐hour‐ahead generating power forecasting for PV system by Yona A. (2008)
{'key': 'e_1_2_8_19_1', 'first-page': '4623', 'volume-title': 'Photovoltaic system power forecasting based on combined grey model and BP neural network', 'author': 'Wang S.', 'year': '2011'} / Photovoltaic system power forecasting based on combined grey model and BP neural network by Wang S. (2011)
10.1504/IJAISC.2008.021264
10.1016/S0196-8904(03)00009-8
10.1016/j.energy.2011.05.006
10.1016/j.apenergy.2010.10.035
10.1016/j.egypro.2011.09.024
10.1016/j.enconman.2012.12.017
10.1260/030952405776234599
{'volume-title': '‘Solar energy forecasting and resource assessment’', 'year': '2013', 'author': 'Kleissl J.', 'key': 'e_1_2_8_27_1'} / ‘Solar energy forecasting and resource assessment’ by Kleissl J. (2013)
10.1115/1.1862266
{'volume-title': 'Short‐term power forecasting by statistical methods for photovoltaic plants in south Italy', 'year': '2013', 'author': 'De Giorgi M.G.', 'key': 'e_1_2_8_29_1'} / Short‐term power forecasting by statistical methods for photovoltaic plants in south Italy by De Giorgi M.G. (2013)
10.1016/j.jhydrol.2003.12.033

Dates

Type	When
Created	11 years, 6 months ago (March 1, 2014, 1:08 a.m.)
Deposited	5 months, 1 week ago (March 21, 2025, 10:42 p.m.)
Indexed	1 week, 6 days ago (Aug. 21, 2025, 2:10 p.m.)
Issued	11 years, 4 months ago (May 1, 2014)
Published	11 years, 4 months ago (May 1, 2014)
Published Online	11 years, 4 months ago (May 1, 2014)
Published Print	11 years, 4 months ago (May 1, 2014)

Funders 1

European Commission 10.13039/501100000780
Region: Europe
gov (National government)
Labels26
1. European Union
2. Comisión Europea
3. Europäische Kommission
4. EU-Kommissionen
5. Euroopa Komisjoni
6. Ευρωπαϊκής Επιτροπής
7. Европейската комисия
8. Evropské komise
9. Commission européenne
10. Choimisiúin Eorpaigh
11. Europskoj komisiji
12. Commissione europea
13. La Commissione europea
14. Eiropas Komisiju
15. Europos Komisijos
16. Európai Bizottságról
17. Europese Commissie
18. Komisja Europejska
19. Comissão Europeia
20. Comisia Europeană
21. Európskej komisii
22. Evropski komisiji
23. Euroopan komission
24. Europeiska kommissionen
25. EC
26. EU
Awards1
1. 285194

BibTeX

@article{De_Giorgi_2014, title={Photovoltaic power forecasting using statistical methods: impact of weather data}, volume={8}, ISSN={1751-8830}, url={http://dx.doi.org/10.1049/iet-smt.2013.0135}, DOI={10.1049/iet-smt.2013.0135}, number={3}, journal={IET Science, Measurement & Technology}, publisher={Institution of Engineering and Technology (IET)}, author={De Giorgi, Maria Grazia and Congedo, Paolo Maria and Malvoni, Maria}, year={2014}, month=may, pages={90–97} }

JSON

{
  "indexed": {
    "date-parts": [
      [
        2025,
        8,
        21
      ]
    ],
    "date-time": "2025-08-21T18:10:54Z",
    "timestamp": 1755799854543,
    "version": "3.40.2"
  },
  "reference-count": 29,
  "publisher": "Institution of Engineering and Technology (IET)",
  "issue": "3",
  "license": [
    {
      "start": {
        "date-parts": [
          [
            2014,
            5,
            1
          ]
        ],
        "date-time": "2014-05-01T00:00:00Z",
        "timestamp": 1398902400000
      },
      "content-version": "vor",
      "delay-in-days": 0,
      "URL": "http://onlinelibrary.wiley.com/termsAndConditions#vor"
    }
  ],
  "funder": [
    {
      "DOI": "10.13039/501100000780",
      "name": "European Commission",
      "doi-asserted-by": "publisher",
      "award": [
        "285194"
      ],
      "id": [
        {
          "id": "10.13039/501100000780",
          "id-type": "DOI",
          "asserted-by": "publisher"
        }
      ]
    }
  ],
  "content-domain": {
    "domain": [],
    "crossmark-restriction": false
  },
  "published-print": {
    "date-parts": [
      [
        2014,
        5
      ]
    ]
  },
  "abstract": "<jats:p>An important issue for the growth and management of grid\u2010connected photovoltaic (PV) systems is the possibility to forecast the power output over different horizons. In this work, statistical methods based on multiregression analysis and the Elmann artificial neural network (ANN) have been developed in order to predict power production of a 960\u00a0kW<jats:sub>P</jats:sub> grid\u2010connected PV plant installed in Italy. Different combinations of the time series of produced PV power and measured meteorological variables were used as inputs of the ANN. Several statistical error measures are evaluated to estimate the accuracy of the forecasting methods. A decomposition of the standard deviation error has been carried out to identify the amplitude and phase error. The skewness and kurtosis parameters allow a detailed analysis of the distribution error.</jats:p>",
  "DOI": "10.1049/iet-smt.2013.0135",
  "type": "journal-article",
  "created": {
    "date-parts": [
      [
        2014,
        3,
        1
      ]
    ],
    "date-time": "2014-03-01T06:08:13Z",
    "timestamp": 1393654093000
  },
  "page": "90-97",
  "source": "Crossref",
  "is-referenced-by-count": 220,
  "title": "Photovoltaic power forecasting using statistical methods: impact of weather data",
  "prefix": "10.1049",
  "volume": "8",
  "author": [
    {
      "given": "Maria Grazia",
      "family": "De Giorgi",
      "sequence": "first",
      "affiliation": [
        {
          "name": "Department of Engineering for Innovation University of Salento Via per Monteroni 73100 Lecce Italy"
        }
      ]
    },
    {
      "given": "Paolo Maria",
      "family": "Congedo",
      "sequence": "additional",
      "affiliation": [
        {
          "name": "Department of Engineering for Innovation University of Salento Via per Monteroni 73100 Lecce Italy"
        }
      ]
    },
    {
      "given": "Maria",
      "family": "Malvoni",
      "sequence": "additional",
      "affiliation": [
        {
          "name": "Department of Engineering for Innovation University of Salento Via per Monteroni 73100 Lecce Italy"
        }
      ]
    }
  ],
  "member": "265",
  "published-online": {
    "date-parts": [
      [
        2014,
        5
      ]
    ]
  },
  "reference": [
    {
      "key": "e_1_2_8_2_1",
      "unstructured": "GSE (Italian manager of energy services). Rapporto statistico; 2011. \u3008http://www.gse.it\u3009"
    },
    {
      "key": "e_1_2_8_3_1",
      "doi-asserted-by": "publisher",
      "DOI": "10.1016/j.renene.2006.06.014"
    },
    {
      "key": "e_1_2_8_4_1",
      "doi-asserted-by": "publisher",
      "DOI": "10.1016/j.solener.2008.02.003"
    },
    {
      "key": "e_1_2_8_5_1",
      "doi-asserted-by": "publisher",
      "DOI": "10.1016/j.renene.2007.09.020"
    },
    {
      "key": "e_1_2_8_6_1",
      "doi-asserted-by": "publisher",
      "DOI": "10.1016/j.solener.2010.02.006"
    },
    {
      "key": "e_1_2_8_7_1",
      "doi-asserted-by": "publisher",
      "DOI": "10.1016/j.solener.2010.07.002"
    },
    {
      "key": "e_1_2_8_8_1",
      "doi-asserted-by": "publisher",
      "DOI": "10.1016/j.energy.2010.10.032"
    },
    {
      "key": "e_1_2_8_9_1",
      "first-page": "773",
      "volume-title": "Forecasting power output for grid\u2010connected photovoltaic power system without using solar radiation measurement",
      "author": "Cai T.",
      "year": "2010"
    },
    {
      "key": "e_1_2_8_10_1",
      "doi-asserted-by": "publisher",
      "DOI": "10.1016/j.solener.2010.08.011"
    },
    {
      "key": "e_1_2_8_11_1",
      "doi-asserted-by": "publisher",
      "DOI": "10.1109/JSTARS.2009.2020300"
    },
    {
      "key": "e_1_2_8_12_1",
      "doi-asserted-by": "publisher",
      "DOI": "10.1016/j.renene.2012.01.108"
    },
    {
      "key": "e_1_2_8_13_1",
      "first-page": "3659",
      "article-title": "Application of recurrent neural network to short\u2010term\u2010ahead generating power forecasting for photovoltaic system",
      "volume": "1",
      "author": "Yona A.",
      "year": "2007",
      "journal-title": "IEEE Power Eng. Soc. Gen. Meet."
    },
    {
      "key": "e_1_2_8_14_1",
      "doi-asserted-by": "publisher",
      "DOI": "10.1016/j.enconman.2009.11.024"
    },
    {
      "key": "e_1_2_8_15_1",
      "doi-asserted-by": "publisher",
      "DOI": "10.1016/j.renene.2006.12.017"
    },
    {
      "key": "e_1_2_8_16_1",
      "doi-asserted-by": "publisher",
      "DOI": "10.1016/j.solener.2009.05.016"
    },
    {
      "key": "e_1_2_8_17_1",
      "doi-asserted-by": "publisher",
      "DOI": "10.1016/j.enconman.2007.12.030"
    },
    {
      "volume-title": "Application of neural network to 24\u2010hour\u2010ahead generating power forecasting for PV system",
      "year": "2008",
      "author": "Yona A.",
      "key": "e_1_2_8_18_1"
    },
    {
      "key": "e_1_2_8_19_1",
      "first-page": "4623",
      "volume-title": "Photovoltaic system power forecasting based on combined grey model and BP neural network",
      "author": "Wang S.",
      "year": "2011"
    },
    {
      "key": "e_1_2_8_20_1",
      "doi-asserted-by": "publisher",
      "DOI": "10.1504/IJAISC.2008.021264"
    },
    {
      "key": "e_1_2_8_21_1",
      "doi-asserted-by": "publisher",
      "DOI": "10.1016/S0196-8904(03)00009-8"
    },
    {
      "key": "e_1_2_8_22_1",
      "doi-asserted-by": "publisher",
      "DOI": "10.1016/j.energy.2011.05.006"
    },
    {
      "key": "e_1_2_8_23_1",
      "doi-asserted-by": "publisher",
      "DOI": "10.1016/j.apenergy.2010.10.035"
    },
    {
      "key": "e_1_2_8_24_1",
      "doi-asserted-by": "publisher",
      "DOI": "10.1016/j.egypro.2011.09.024"
    },
    {
      "key": "e_1_2_8_25_1",
      "doi-asserted-by": "publisher",
      "DOI": "10.1016/j.enconman.2012.12.017"
    },
    {
      "key": "e_1_2_8_26_1",
      "doi-asserted-by": "publisher",
      "DOI": "10.1260/030952405776234599"
    },
    {
      "volume-title": "\u2018Solar energy forecasting and resource assessment\u2019",
      "year": "2013",
      "author": "Kleissl J.",
      "key": "e_1_2_8_27_1"
    },
    {
      "key": "e_1_2_8_28_1",
      "doi-asserted-by": "publisher",
      "DOI": "10.1115/1.1862266"
    },
    {
      "volume-title": "Short\u2010term power forecasting by statistical methods for photovoltaic plants in south Italy",
      "year": "2013",
      "author": "De Giorgi M.G.",
      "key": "e_1_2_8_29_1"
    },
    {
      "key": "e_1_2_8_30_1",
      "doi-asserted-by": "publisher",
      "DOI": "10.1016/j.jhydrol.2003.12.033"
    }
  ],
  "container-title": "IET Science, Measurement &amp; Technology",
  "original-title": [],
  "language": "en",
  "link": [
    {
      "URL": "https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-smt.2013.0135",
      "content-type": "application/pdf",
      "content-version": "vor",
      "intended-application": "text-mining"
    },
    {
      "URL": "https://onlinelibrary.wiley.com/doi/full-xml/10.1049/iet-smt.2013.0135",
      "content-type": "application/xml",
      "content-version": "vor",
      "intended-application": "text-mining"
    },
    {
      "URL": "https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-smt.2013.0135",
      "content-type": "unspecified",
      "content-version": "vor",
      "intended-application": "similarity-checking"
    }
  ],
  "deposited": {
    "date-parts": [
      [
        2025,
        3,
        22
      ]
    ],
    "date-time": "2025-03-22T02:42:40Z",
    "timestamp": 1742611360000
  },
  "score": 1,
  "resource": {
    "primary": {
      "URL": "https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/iet-smt.2013.0135"
    }
  },
  "subtitle": [],
  "short-title": [],
  "issued": {
    "date-parts": [
      [
        2014,
        5
      ]
    ]
  },
  "references-count": 29,
  "journal-issue": {
    "issue": "3",
    "published-print": {
      "date-parts": [
        [
          2014,
          5
        ]
      ]
    }
  },
  "alternative-id": [
    "10.1049/iet-smt.2013.0135"
  ],
  "URL": "http://dx.doi.org/10.1049/iet-smt.2013.0135",
  "relation": {},
  "ISSN": [
    "1751-8822",
    "1751-8830"
  ],
  "subject": [],
  "container-title-short": "IET Science Measure &amp;amp; Tech",
  "published": {
    "date-parts": [
      [
        2014,
        5
      ]
    ]
  }
}