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RESEARCH PRODUCT

A new method for forecasting energy output of a large-scale solar power plant based on long short-term memory networks a case study in Vietnam

Binh Van DoanDario Di CaraLinh Duy BuiNinh Quang NguyenQuang Dinh NguyenEleonora Riva Sanseverino

subject

Scale (ratio)Computer scienceLarge scale solar power plant020209 energy020208 electrical & electronic engineeringEnergy Engineering and Power Technology02 engineering and technologySet (abstract data type)Mean absolute percentage errorElectricity generationSolar power plantArtificial IntelligenceStatistics0202 electrical engineering electronic engineering information engineeringLong short-term memoryElectrical and Electronic EngineeringTime seriesPV power plantForecasting PV powerEnergy (signal processing)Test data

description

Abstract This paper proposes a new model for short-term forecasting power generation capacity of large-scale solar power plant (SPP) in Vietnam considering the fluctuations of weather factors when applying the Long Short-Term Memory networks (LSTM) algorithm. At first, a configuration of the model based on the LSTM algorithm is selected in accordance with the weather and operating conditions of SPP in Vietnam. Not only different structures of LSTM model but also other conventional forecasting methods for time series data are compared in terms of error accuracy of forecast on test data set to evaluate the effectiveness and select the most suitable LSTM configuration. The most suitable configuration has been selected and applied on Thanh Thanh Cong No 1 (TTC) SPP with 2 input cases: real historical weather data and forecasted weather data. The results show that second case gives a much larger Mean Absolute Percentage Error (MAPE) than that of first case (10.857% versus 3.491%). Based on above experiment, new additional features are proposed to improve the selected LSTM model precision and cope with the problem of error due to weather forecast data. The result of the application of the new prediction model for TTC solar plant indicates that the MAPE is reduced from 10.857% to 9.881%.

yearjournalcountryeditionlanguage
2021-10-01
10.1016/j.epsr.2021.107427http://hdl.handle.net/10447/516209