Multivariate CNN-LSTM model for wind power forecast and input variables correlation analysis based on SHAPLEY values (2023)
Presentation / Conference
Pina-Gongora, D. C., & Kazemtabrizi, B. (2023, May). Multivariate CNN-LSTM model for wind power forecast and input variables correlation analysis based on SHAPLEY values. Paper presented at Wind Energy Science Conference (WESC) 2023, Glasgow, UK

Wind Power forecast based on the use of modern Deep Neural Network (DNN) methods is being explored as a tool to support decision-making by wind farm developers and operators for investment, grid integration and operational planning of the site. Techn... Read More about Multivariate CNN-LSTM model for wind power forecast and input variables correlation analysis based on SHAPLEY values.

Performance comparison of Probabilistic and Artificial Neural Network Models for Long-sequence Generation of Wind Speed Forecasts (2022)
Conference Proceeding
Pina-Gongora, D. C., Kazemtabrizi, B., & Crabtree, C. (2022). Performance comparison of Probabilistic and Artificial Neural Network Models for Long-sequence Generation of Wind Speed Forecasts. . https://doi.org/10.1049/icp.2022.2777

This paper presents a new method for generating long-sequence wind speed time-series forecasts for purposes of offshore wind farm asset and operations planning. Our goal is to develop a planning decision support tool with which wind farm planners and... Read More about Performance comparison of Probabilistic and Artificial Neural Network Models for Long-sequence Generation of Wind Speed Forecasts.