Publication:
Solar irradiance measurement instrumentation and power solar generation forecasting based on Artificial Neural Networks (ANN): A review of five years research trend

dc.citedby84
dc.contributor.authorPazikadin A.R.en_US
dc.contributor.authorRifai D.en_US
dc.contributor.authorAli K.en_US
dc.contributor.authorMalik M.Z.en_US
dc.contributor.authorAbdalla A.N.en_US
dc.contributor.authorFaraj M.A.en_US
dc.contributor.authorid57214365739en_US
dc.contributor.authorid56167376800en_US
dc.contributor.authorid36130958600en_US
dc.contributor.authorid57189362613en_US
dc.contributor.authorid56050971600en_US
dc.contributor.authorid57193631359en_US
dc.date.accessioned2023-05-29T08:10:18Z
dc.date.available2023-05-29T08:10:18Z
dc.date.issued2020
dc.descriptionElectric power system control; Electric power transmission networks; Forecasting; Hybrid systems; Information management; Instrument errors; Neural networks; Photovoltaic cells; Solar concentrators; Solar energy; Solar radiation; Forecasting accuracy; Irradiance; Photovoltaic; Renewable energy source; Solar; Solar irradiance measurement; Solar photovoltaic plants; Solar photovoltaic power generations; Solar power generation; accuracy assessment; artificial neural network; calibration; design; forecasting method; instrumentation; power generation; solar power; solar radiation; article; artificial neural network; calibration; forecasting; human; Scopus; weather; Scopusen_US
dc.description.abstractThe increased demand for solar renewable energy sources has created recent interest in the economic and technical issues related to the integration of Photovoltaic (PV) into the grid. Solar photovoltaic power generation forecasting is a crucial aspect of ensuring optimum grid control and power solar plant design. Accurate forecasting provides significant information to grid operators and power system designers in generating an optimal solar photovoltaic plant and to manage the power of demand and supply. This paper presents an extensive review on the implementation of Artificial Neural Networks (ANN) on solar power generation forecasting. The instrument used to measure the solar irradiance is analysed and discussed, specifically on studies that were published from February 1st, 2014 to February 1st, 2019. The selected papers were obtained from five major databases, namely, Direct Science, IEEE Xplore, Google Scholar, MDPI, and Scopus. The results of the review demonstrate the increased application of ANN on solar power generation forecasting. The hybrid system of ANN produces accurate results compared to individual models. The review also revealed that improvement forecasting accuracy can be achieved through proper handling and calibration of the solar irradiance instrument. This finding indicates that improvements in solar forecasting accuracy can be increased by reducing instrument errors that measure the weather parameter. � 2020 Elsevier B.V.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo136848
dc.identifier.doi10.1016/j.scitotenv.2020.136848
dc.identifier.scopus2-s2.0-85078696116
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85078696116&doi=10.1016%2fj.scitotenv.2020.136848&partnerID=40&md5=2b20c89a77952d4e09d9ec452ca54f66
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25507
dc.identifier.volume715
dc.publisherElsevier B.V.en_US
dc.sourceScopus
dc.sourcetitleScience of the Total Environment
dc.titleSolar irradiance measurement instrumentation and power solar generation forecasting based on Artificial Neural Networks (ANN): A review of five years research trenden_US
dc.typeArticleen_US
dspace.entity.typePublication
Files
Collections