References

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Альтернативная энергетика и экология (ISJAEE)

Alternative Energy and Ecology (ISJAEE)

1608-8298

Международный издательский дом научной периодики "Спейс

10.15518/isjaee.2017.16-18.030-042

alternative-1083

Research Article

ВОЗОБНОВЛЯЕМАЯ ЭНЕРГЕТИКА. СОЛНЕЧНАЯ ЭНЕРГЕТИКА

ИНТЕЛЛЕКТУАЛЬНАЯ СИСТЕМА ПРОГНОЗИРОВАНИЯ РАБОТЫ СОЛНЕЧНЫХ ЭЛЕКТРОСТАНЦИЙ

INTELLIGENT SYSTEM FOR FORECASTING OF THE SOLAR POWER PLANTS WORK

Абруков

В. С.

Abrukov

V. S.

Виктор Сергеевич Абруков - докторр физико-математических наук, зав. кафедрой прикладной физики и нанотехнологий, Чувашский государственный университет имени И.Н. Ульянова.

д. 15, Московский пр-т, Чебоксары, Чувашская Республика.

Victor Abrukov - D.Sc. (physics and mathematics), Head of Department of Applied Physics and Nanotechnology, Chuvash State University named after I.N. Ulyanova.

15 Moskovsky ave., Cheboksary, Chuvash Republic, 428015, Russia.

abrukov@yandex.ru

Кочаков

В. Д.

Kochakov

V. D.

Кочаков Валерий Данилович - кандидат технических наук, профессор кафедры прикладной физики и нанотехнологий, Чувашский государственный университет имени И.Н. Ульянова.

д. 15, Московский пр-т, Чебоксары, Чувашская Республика.

Valery Kochakov - Ph.D. (enginering), Professor at Department of Applied Physics and Nanotechnology, Chuvash State University named after I.N. Ulyanova.

15 Moskovsky ave., Cheboksary, Chuvash Republic, 428015, Russia.

Абруков

С. В.

Abrukov

S. V.

Абруков Сергей Викторович - младший научный сотрудник кафедры прикладной физики и нанотехнологий, Чувашский государственный университет имени И.Н. Ульянова.

д. 15, Московский пр-т, Чебоксары, Чувашская Республика.

Sergei Abrukov - Junior Researcher at Department of Applied Physics and Nanotechnology, Chuvash State University named after I.N. Ulyanova.

15 Moskovsky ave., Cheboksary, Chuvash Republic, 428015, Russia.

Ануфриева

Д. А.

Anufrieva

D. A.

Ануфриева Дарья Александровна - младший научный сотрудник кафедры прикладной физики и нанотехнологий, Чувашский государственный университет имени И.Н. Ульянова.

д. 15, Московский пр-т, Чебоксары, Чувашская Республика.

Darya Anufrieva - Junior Researcher at Department of Applied Physics and Nanotechnology, Chuvash State University named after I.N. Ulyanova.

15 Moskovsky ave., Cheboksary, Chuvash Republic, 428015, Russia.

Васильев

А. И.

Vasilyev

A. I.

Васильев Алексей Иванович - инженер-исследователь кафедры прикладной физики и нанотехнологий, Чувашский государственный университет имени И.Н. Ульянова.

д. 15, Московский пр-т, Чебоксары, Чувашская Республика.

Alexey Vasilyev - ResearchEngineer at Department of Applied Physics and Nanotechnology, The Chuvash State University named after I.N. Ulyanov.

15 Moskovsky ave., Cheboksary, Chuvash Republic, 428015, Russia.

Смирнов

А. В.

Smirnov

A. V.

Смирнов Александр Вячеславович - инженер Чувашского государственного университета имени И.Н. Ульянова, председатель Ассоциации молодых физиков Чувашии.

д. 15, Московский пр-т, Чебоксары, Чувашская Республика.

Alexander Smirnov - Engineer at The Chuvash State University named after I.N. Ulyanov, Chairman of the Association of Young Physicists of Chuvashia.

15 Moskovsky ave., Cheboksary, Chuvash Republic, 428015, Russia.

Чувашский государственный университет им. И.Н. Ульянова.РоссияChuvash State University named after I.N. Ulyanov.Russian Federation

2017

13092017

016-183042

2017

Международный издательский дом научной периодики "Спейс

https://www.isjaee.com/jour/about/submissions#copyrightNotice

https://www.isjaee.com/jour/article/view/1083

Представлены методы и технологии создания вычислительных систем прогнозирования работы солнечных электростанций (СЭ) в зависимости от параметров метеоусловий. Для создания систем прогнозирования использовались искусственные нейронные сети, включенные в аналитическую платформу “Deductor”, которая обладает широким спектром методов предобработки, предварительного анализа и моделирования данных. В основе этих систем лежат результаты уникального непрерывного четырехлетнего многопараметрического мониторинга характеристик работы СЭ и параметров метеоусловий. Системой мониторинга регистрировались следующие величины: напряжение, ток в цепи нагрузки и мощность, солнечная радиация, внешняя температура, влажность, точка росы, скорость ветра, направление ветра, индекс охлаждения ветром, индекс нагрева, индекс «температура + влажность + ветер», индекс «температура + влажность + ветер + солнце», атмосферное давление, ультрафиолетовый индекс, индекс испарения. Дополнительно определялись две характеристики работы СЭ: плотность мощности и значение коэффициента преобразования солнечной энергии в электрическую. Описаны два варианта созданных многофакторных вычислительных моделей прогнозирования плотности мощности и коэффициента преобразования в зависимости от параметров метеоусловий. Первый вариант использует полный набор регистрируемых параметров метеоусловий, второй вариант – ограниченный набор параметров метеоусловий по данным прогноза Гидрометцентра РФ. Приведены примеры применения обоих вариантов систем прогнозирования. Полученные системы могут быть полезными не только для непосредственного прогнозирования работы СЭ, но и для районирования территории РФ с точки зрения перспектив развития солнечной энергетики и технико-экономического обоснования строительства СЭ. Важность такого подхода к прогнозированию заключается в том, что речь идет не об общепринятом районировании территории РФ по уровню солнечной радиации, а по плотности мощности и коэффициента преобразования солнечной энергии в электрическую. Кроме того, предложенный подход может быть применен для прогнозирования работы ветровых электростанций; термодинамических установок, использующих энергию солнца, и других устройств альтернативной энергетики.

The paper presents the methods and technologies of development of the computational systems for forecasting of solar power plants (SP) work in the meteorological conditions dependence. The artificial neural networks included into the analytical platform “Deductor” which has a wide range of methods for the pre-treatment of data, methods for preliminary data analysis, and data modeling are used to create forecasting systems. These systems are based on the results of the unique continuous four-year multi-parameter monitoring of SP characteristics and meteorological conditions. The following variables: voltage, current in the load circuit and power, solar radiation, external temperature, humidity, dew point, wind speed, wind direction, wind chill index, heating index, index of “temperature + humidity + wind”, index of “temperature + humidity + wind + solar radiation”, atmospheric pressure, ultraviolet index, and index of evaporation – have been registering by the monitoring system. The power density and the value of the conversion coefficient of solar energy into electrical energy are determined additionally. The paper describes two variants of the multifactor computational models for forecasting the power density and the conversion coefficient in the meteorological conditions dependence. The first variant uses the full set of recorded meteorological conditions. The second variant uses the limited set of meteorological conditions available from the forecast of the Hydro Meteorological Center of the Russian Federation. The paper gives the application examples of both variants of forecasting systems. These forecasting systems can be used not only for the direct prediction of the SP work, but also for the zoning of the Russian Federation territory from the perspectives of SP building. The importance of this approach lies in the fact that it is not a question of the generally accepted zoning of the Russian Federation territory in the solar radiation level dependence, but of zoning in terms of power density and the conversion coefficient of solar energy into electrical one. The proposed approach can be used to forecast of wind power plants work, work of thermodynamic installations using solar energy, and other devices of alternative energetic.

солнечная энергетикасолнечные электростанцииплотность мощностикоэффициент преобразованияпрогнозированиеметоды интеллектуального анализа данныхискусственные нейронные сети

solar powersolar power plantspower densitytransformation ratioforecastingdata mining methodsartificial neural

References1

Yadav, Amit Kumar. Solar radiation prediction using Artificial Neural Network techniques: A review / Amit Kumar Yadav, S.S. Chandel // Renewable and Sustainable Energy Reviews. – 2014. – Vol. 33. – P. 772–781.

[1] Amit Kumar Yadav, Chandel S.S. Solar radiation prediction using Artificial Neural Network techniques: A review. Renewable and Sustainable Energy Reviews, 2014;33:772–781 (in Eng.).

Qazi, Atika. The artificial neural network for solar radiation prediction and designing solar systems: a systematic literature review / Atika Qazi [et al.] // Journal of Cleaner Production. – 2015. – Vol. 104. – P. 1–12.

[2] Atika Qazi [et al.]. The artificial neural network for solar radiation prediction and designing solar systems: a systematic literature review. Journal of Cleaner Production, 2015;104:1–12 (in Eng.).

AI-Alawi, S.M. An ANN. based approach for predicting global radiation in locations with no direct measurement instrumentation [Text] / S.M.AI-Alawi, H.A AI-Hinai // Renewable Energy. – 1998. – Vol. 14. – No. 1–4. – P. 199–204.

[3] AI-Alawi S.M., AI-Hinai H.A. An ANN. based approach for predicting global radiation in locations with no direct measurement instrumentation. Renewable Energy, 1998;14(1–4):199–204 (in Eng.).

Sözen, A. Use of artificial neural networks for mapping of solar potential in Turkey / A. Sözen [et al.] //Applied Energy. – 2004. – Vol. 77. – P. 273–86.

[4] Sözen A., Arcaklioğlu E., Özalp M., Kanit E. Use of artificial neural networks for mapping of solar potential in Turkey. Applied Energy, 2004;77:273–86 (in Eng.).

Sözen, A. Estimation of solar potential in Turkey by artificial neural networks using meteorological and geographical data [Text] / A. Sözen, E. Arcaklioğlu, M. Özalp // Energy Conversion and Management. – 2004. – Vol. 45. – P. 3033–52.

[5] Sözen A., Arcaklioğlu E., Özalp M. Estimation of solar potential in Turkey by artificial neural networks using meteorological and geographical data. Energy Conversion and Management, 2004;45:3033–52 (in Eng.).

Rehman, S. Estimation of diffuse fraction of global solar radiation using artificial neural networks [Text] / S. Rehman, M. Mohandes // Energy Sources, Part A. – 2009.– No. 31. – P. 974–84.

[6] Rehman S., Mohandes M. Estimation of diffuse fraction of global solar radiation using artificial neural networks. Energy Sources, Part A, 2009;31:974–84 (in Eng.).

Lazzús, J.A. Estimation of global solar radiation over the City of LaSerena (Chile) using aneural network [Text] / J.A Lazzús, A.P. Ponce, J. Marín // Applied Solar Energy. – 2011. – Vol. 47. – No 1. – P. 66–73.

[7] Lazzús J.A., Ponce A.P., Marín J. Estimationofglobalsolarradiationoverthe City of LaSerena (Chile) using aneural network. Applied Solar Energy, 2011;47(1):66–73 (in Eng.).

Azeez, M.A. Artificial neural network estimation of global solar radiation using meteorological parameters in Gusau,Nigeria [Text] / M.A. Azeez //Archives of Applied Science Research. – 2011. – Vol. 3. – No 2. – P. 586–95.

[8] Azeez M.A. Artificial neural network estimation of global solar radiation using meteorological parameters in Gusau,Nigeria. Archives of Applied Science Research, 2011;3(2):586–95 (in Eng.).

Linares-Rodríguez, A. Generation of synthetic daily global solar radiation data based on ERA- interim reanalysis and artificial neural networks [Text] / A. Linares-Rodríguez Ruiz, J.A. Arias, D. Pozo-Vázquez, J. Tovar Pescador // Energy. – 2011. – Vol. 36. – P. 5356–65.

[9] Linares-Rodríguez A., Arias J.A., Pozo-Vázquez D., Tovar Pescador J. Generation of synthetic daily global solar radiation data based on ERA- interim reanalysis and artificial neural networks. Energy, 2011;36:5356–65 (in Eng.).

Şenkal, O. Estimation of solar radiation over Turkey using artificial neural network and satellite data [Text] / O. Şenkal, T. Kuleli // Applied Energy. – 2009. – Vol. 86. – P. 1222–1228.

[10] Şenkal O., Kuleli T. Estimation of solar radiation over Turkey using artificial neural network and satellite data. Applied Energy, 2009;86:1222–1228 (in Eng.).

Hasni, A. Estimating global solar radiation using artificial neural network and climate data in the southwestern region of Algeria [Text] / A. Hasni [et al.] // Energy Procedia. – 2012. – Vol. 18. – P. 531–537.

[11] Hasni A., Sehli A., Draoui B., Bassou A., Amieur B. Estimating global solar radiation using artificial neural network and climate data in the south- western region of Algeria. Energy Procedia, 2012;18:531–537 (in Eng.).

Yadav, A.K. Artificial neural network based prediction of solar radiation for Indian stations [Text] / A.K. Yadav, S.S. Chandel // International Journal of Computer Applications. – 2012. – Vol. 50. – P. 1–4.

[12] Yadav A.K., Chandel S.S. Artificial neural network based prediction of solar radiation for Indian stations. International Journal of Computer Applications, 2012;50:1–4 (in Eng.).

Elminir, H. K. Estimation of solar radiation components incident on Helwansite using neural networks [Text] / H. K. Elminir, F.F. Areed., T.S. Elsayed // Solar Energy. – 2005. – Vol. 79. – P. 270–279.

[13] Elminir H.K., Areed F.F., Elsayed T.S. Estimation of solar radiation components incident on Helwansite using neural networks. Solar Energy, 2005;79:270–279 (in Eng.).

Azadeh, A. An integrated artificial neural networks approach for predicting global radiation [Text] / A. Azadeh, A. Maghsoudi, S. Sohrabkhani // Energy Conversion and Management. – 2009. – Vol. 50. – P. 1497–1505.

[14] Azadeh A., Maghsoudi A., Sohrabkhani S. An integrated artificial neural networks approach for pr edicting global radiation. Energy Conversion and Management, 2009;50:1497–1505 (in Eng.).

Sözen, A. Solar potential in Turkey [Text] / A. Sözen, E. Arcaklioğlu // Applied Energy. – 2005. – Vol. 80. – P. 35–45.

[15] Sözen A., Arcaklioğlu E. Solar potential in Turkey. Applied Energy, 2005;80:35–45 (in Eng.).

Rumbayan, M. Mapping of solar energy potential in Indonesia using artificial neural network and geographical information system [Text] / M. Rumbayan, A. Abudureyimu, K. Nagasaka // Renewable and Sustainable Energy Reviews. – 2012. – Vol. 16. – P. 1437–1449.

[16] Rumbayan M., Abudureyimu A., Nagasaka K.Mapping of solar energy potential in Indonesia using artificial neural network and geographical information system. Renewable and Sustainable Energy Reviews, 2012;16:1437–1449 (in Eng.).

Şenkal, O. The estimation of solar radiation for different time periods [Text] / O.Şenkal, M. Şahin, V.Peštemalci // Energy Sources, Part A. – 2010. – Vol. 32. – P. 1176–1184.

[17] Şenkal O., Şahin M., Peštemalci V. The estimation of solar radiation for different time periods. Energy Sources, Part A, 2010;32:1176–1184 (in Eng.).

Muammer, O. Estimation of global solar radiation using ANN over Turkey [Text] / Muammer Ozgoren, Mehmet Bilgili, Besir Sahin // Expert Syst. Appl. – 2012. – Vol. 39. – No 5. – P. 5043–5051.

[18] Ozgoren Muammer, Bilgili Mehmet, Sahin Besir Estimation of global solar radiation using ANN over Turkey. Expert Syst. Appl., 2012;39(5):5043–5051 (in Eng.).

Yingni, Jiang. Prediction of monthly mean daily diffuse solar radiation using artificial neural networks and comparison with other empirical models [Text] / Yingni Jiang // Energy Policy. – 2008. – Vol. 36. – No10. – P. 3833–3837.

[19] Jiang Yingni. Prediction of monthly mean daily diffuse solar radiation using artificial neural networks and comparison with other empirical models. Energy Policy, 2008;36(10):3833–3837 (in Eng.).

Shah, A. Assessment of diffuse solar energy under general sky condition using artificial neural network [Text] / Alam Shah, S.C. Kaushik, S.N. Garg // Appl. Energy. – 2009. – Vol. 86. – No 4. – P. 554–564.

[20] Alam Shah, Kaushik, S.C., Garg, S.N. Assessment of diffuse solar energy under general sky condition using artificial neural network. Appl. Energy, 2009;86(4):554–564 (in Eng.).

Koca, A. Estimation of solar radiation using artificial neural networks with different input parameters for Mediterranean region of Anatolia in Turkey [Text] / Ahmet Koca [et al.] // Expert Syst. Appl. – 2011. – Vol. 38. – No. 7. – P. 8756–8762.

[21] Koca Ahmet, Oztop Hakan F., Varol Yasin Koca, Gonca Ozmen. Estimation of solar radiation using artificial neural networks with different input parameters for Mediterranean region of Anatolia in Turkey. Expert Syst. Appl., 2011;38(7):8756–8762 (in Eng.).

Chen, S.X. Solar radiation forecast based on fuzzy logic and neural networks [Text] / S.X. Chen, H.B. Gooi, M.Q. Wang // Renew. Energy. – 2013. – Vol. 60. – P. 195–201.

[22] Chen S.X., Gooi H.B., Wang M.Q. Solar radiation forecast based on fuzzy logic and neural networks. Renew. Energy, 2013;60:195–201 (in Eng.).

Adel Mellit. A 24-h forecast of solar irradiance using artificial neural network: Application for performance prediction of a grid-connected PV plant at Trieste, Italy [Text] / Mellit Adel // Solar Energy. – 2010. – Vol. 84. – No 5. – P. 807–821

[23] Adel Mellit. A 24-h forecast of solar irradiance using artificial neural network: Application for performance prediction of a grid-connected PV plant at Trieste, Italy. Solar Energy, 2010;84(5):807–821 (in Eng.).

Wai Kean Yap. An off-grid hybrid PV/diesel model as a planning and design tool, incorporating dynamic and ANN modelling techniques [Text] / Wai Kean Yap, Vishy Karri // Renewable Energy. – 2015. – Vol. 78. – P. 42–50.

[24] Wai Kean Yap, Vishy Karri. An off-grid hybrid PV/diesel model as a planning and design tool, incorporating dynamic and ANN modelling techniques. Renewable Energy, 2015;78:42–50 (in Eng.).

Мустафаев, А.Г. Нейросетевая модель прогнозирования уровня солнечной энергии для задач альтернативной энергетики [Текст] / А.Г. Мустафаев // Программные системы и вычислительные методы. – 2016. – № 2. – С. 150–157.

[25] Mustafaev A.G. Neural network model for predicting the level of solar energy for alternative energy tasks (Neirosetevaya model' prognozirovaniya urovnya solnechnoi energii dlya zadach al'ternativnoi energetiki).Programmnye sistemy i vychislitel'nye metody,2016;(2):150–157 (in Russ.).

Абруков, В.С. Создание баз знаний солнечных электростанций [Текст] / В.С. Абруков [и др.] // Международный научный журнал «Альтернативная энергетика и экология» (ISJAEE). – 2015. – Т. 19. – № 183.– С. 29–41.

[26] Abrukov V.S., Kochakov V.D., Ivanitskii A.Yu., Vasilyev A.I., Smirnov A.V., Abrukov S.V. Creation of knowledge bases of solar power plants (Sozdanie baz znanii solnechnykh elektrostantsii). International Scientific Journal for Alternative Energy and Ecology (ISJAEE), 2015;19(183):29 – 41 (in Russ.).

Abrukov, V. Knowledge-Based System is a Goal and a Tool for Basic and Applied Research [Text] / V. Abrukov [et al.] // Conference Proceedings of 9th International Conference on Application of Information and Communication Technologies – AICT Rostov-on-Don , The Institute of Electrical and Electronics Engineers, Inc. – 2015. – P. 60–63.

[27] Abrukov V., Kochakov V., Smirnov A., Abrukov S., Anufrieva D.Knowledge-Based System is a Goal and a Tool for Basic and Applied Research. Conference Proceedings of 9th International Conference on Application of Information and Communication Technologies – AICT Rostov-on-Don , The Institute of Electrical and Electronics Engineers, Inc., 2015, pp. 60–63 (in Eng.).

Abrukov, V.S. Сreation of propellant combustion models by means of data mining tools / V.S. Abrukov [et al.] // International Journal of Energetic Materials and Chemical Propulsion. – 2010. – № 5. – С. 385 396.

[28] Abrukov V.S., Karlovich E.V., Afanasyev V.N., Semenov Y.V., Abrukov S.V. Сreation of propellant combustion models by means of data mining tools. International Journal of Energetic Materials and Chemical Propulsion, 2010;(5):385 396 (in Eng.).

Абруков, В.С. База знаний процессов горения: будущее мира горения [Текст] / В.С. Абруков [и др.] // Вестник Чувашского университета. – 2013. – № 3. – С. 46–52.

[29] Abrukov V.S., Abrukov S.V., Karlovich E.V., Semenov Yu.V. Knowledge base of combustion processes: the future of the combustion world (Baza znanii protsessov goreniya: budushchee mira goreniya). Vestnik Chuvashskogo universiteta, 2013;(3):46 52 (in Russ.).

Абруков, В.С. Data Mining в научных исследованиях / В.С. Абруков // Сборник материалов I Всероссийской научной конференции «Наноструктурированные материалы и преобразовательные устройства для солнечных элементов 3-го поколения» (Чебоксары). – 2013. – С. 11–17.

[30] Abrukov V.S. Data Mining in scientific research (Data Mining v nauchnykh issledovaniyakh). Sbornik materialov I Vserossiiskoi nauchnoi konferentsii «Nanostrukturirovannye materialy i preobrazovatel'nye ustroistva dlya solnechnykh elementov 3-go pokoleniya» (Cheboksary), 2013, pp. 11 17 (in Russ.).

Абруков, В.С. База знаний: эксперимент, интеллектуальный анализ данных, искусственные нейронные сети / В.С. Абруков [и др.] // Сборник трудов 2 Всероссийской научной конференции «Наноструктурированные материалы и преобразовательные устройства для солнечной энергетики 3-го поколения» (Чебоксары). – 2014. – С. 15–21.

[31] Abrukov V.S., Abrukov S.V., Smirnov A.V., Karlovich E.V. Knowledge base: experiment, data mining, artificial neural networks (Baza znanii: eksperiment, intellektual'nyi analiz dannykh, iskusstvennye neironnye seti). Sbornik trudov II Vserossiiskoi nauchnoi konferentsii “Nanostrukturirovannye materialy i preobrazovatel'nye ustroistva dlya solnechnoi energetiki 3-go pokoleniya” (Cheboksary), 2014, 15 21 (in Russ.).

Абруков, В.С. Методы интеллектуального анализа данных при создании баз знаний [Текст] / В.С. Абруков, [и др.] // Вестник Чувашского университета. – 2015. – № 1. – С. 140–146.

[32] Abrukov V.S., Abrukov S.V., Smirnov A.V., Karlovich E.V.Methods of data mining for the creation of knowledge bases (Metody intellektual'nogo analiza dannykh pri sozdanii baz znanii). Vestnik Chuvashskogo universiteta, 2015;(1):140 146 (in Russ.).

Tokmoldin, N. Evaluation of the annual electric energy output of an a-Si:H solar cell in various regions of the CIS countries [Text] / N. Tokmoldin [et al.] // Energy Policy. – 2014. – Vol. 68. – P. 116 122.

[33] Tokmoldin N., Kryuchenko Yu.V., Sachenko A.V., Bobyl A.V., Kostylyov V.P., Sokolovskyi I.O., Terukov E.I., Tokmoldin S.Z., Smirnov A.V. Evaluation of the annual electric energy output of an a-Si:H solar cell in various regions of the CIS countries. Energy Policy. 2014;68:116 122 (in Eng.).

Бобыль, А.В. Технико-экономические аспекты сетевой солнечной энергетики в России [Текст] / А.В.Бобыль [и др.] // Журнал технической физики. – 2014. – № 4. – С. 85–93.

[34] Bobyl' A.V., Kiseleva S.V., Kochakov V.D., Orekhov D.L., Tarasenko A.B., Terukova E.E. Technical and economic aspects of solar network power in Russia (Tekhniko-ekonomicheskie aspekty setevoi solnechnoi energetiki v Rossii). Zhurnal tekhnicheskoi fiziki, 2014;(4):85 93 (in Russ.).

The authors declare that there are no conflicts of interest present.