Optimal Design of Hybrid Renewable Energy Systems Considering Weather Forecasting Using Recurrent Neural Networks

• Published in: Energies (Basel) Vol. 15; no. 23; p. 9045
• Main Authors: Medina-Santana, Alfonso Angel, Cárdenas-Barrón, Leopoldo Eduardo
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2022
• Subjects: Air quality management; Algorithms; Alternative energy sources; Capital costs; Carbon; Clustering; Decision making; Deep learning; Electric power production; Electricity; Emissions; Energy management; Environmental aspects; Forecasting techniques; GHI; Information dissemination; Linear programming; Long short-term memory; long-term forecasting; LSTM; Mathematical optimization; Methods; Neural networks; non-linear optimization; Optimization; Power; Recurrent neural networks; Renewable resources; RNN; Rural areas; Rural communities; Sizing; solar energy; Sustainable yield; Time series; Weather forecasting; Mexico
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en15239045

Lack of electricity in rural communities implies inequality of access to information and opportunities among the world’s population. Hybrid renewable energy systems (HRESs) represent a promising solution to address this situation given their portability and their potential contribution to avoiding carbon emissions. However, the sizing methodologies for these systems deal with some issues, such as the uncertainty of renewable resources. In this work, we propose a sizing methodology that includes long short-term memory (LSTM) cells to predict weather conditions in the long term, multivariate clustering to generate different weather scenarios, and a nonlinear mathematical formulation to find the optimal sizing of an HRES. Numerical experiments are performed using open-source data from a rural community in the Pacific Coast of Mexico as well as open-source programming frameworks to allow their reproducibility. We achieved an improvement of 0.1% in loss of load probability in comparison to the seasonal naive method, which is widely used in the literature for this purpose. Furthermore, the RNN training stage takes 118.42, 2103.35, and 726.71 s for GHI, wind, and temperature, respectively, which are acceptable given the planning nature of the problem. These results indicate that the proposed methodology is useful as a decision-making tool for this planning problem.