Year: 2024
Type: conference-paper
Abstract: Forecasting algorithms for photovoltaic (PV) power generation play an important role in energy management systems. Nevertheless, the precision of machine learning models is significantly compromised when historical data is limited. This situation is challenging for new plants for which a long history of measurements is not yet available. The unpredictable nature of the weather gives the perception that a competitive forecast requires a substantial amount of data and a very complicated algorithm. However, in this manuscript, it was found that using five historical days for the inverse quantification of uncertainty, can implicitly describe complex non-linear relationships between last five-day records and day-ahead PV power generation. The proposed approach learns the emerging patterns across various seasons throughout the year without relying on exogenous data such as air temperature, wind speed, pressure, cloud cover, and relative humidity. Results using real-world data collected at the microgrid of the University of Campinas (UNICAMP) confirm that our proposed model outperforms previous state-of-the-art deep learning models as Long short-term memory (LSTM), Gated Recurrent Unit (GRU) and traditional Autoregressive Integrated Moving Average (ARIMA) statistical model, using limited data. The proposed approach is flexible and can be easily adapted to other PV power generation systems with limited data. The source code is available at https://github.com/byronacunia/Day-Ahead-Photovoltaic-Power-Forecasting-with-Limited-Data.git
Source: IEEE URUCON
Authors: Diana Estefanía Chérrez Barragán, Byron Alejandro Acuña Acurio, Juan López, Hugo Morais, Cindy P. Guzman, Luiz Silva
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funders: European Commission, Fundação de Amparo à Pesquisa do Estado de São Paulo, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Financiadora de Estudos e Projetos, Agência Nacional de Energia Elétrica, European Climate, Infrastructure and Environment Executive Agency, Fundação para a Ciência e a Tecnologia
Awards: G1740015287, Interdisciplinary research activities in electric smart grids, Instituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa, G2477019724, G4478570843, Use of open-source P2P energy sharing platforms for energy democratization, G5696302295, G6438247056, G6893826488, G6997658380, G7727506858
OpenAlex ID: W4407010856