RT journal article
T1 Smart Urban Wind Power Forecasting: Integrating Weibull Distribution, Recurrent Neural Networks, and Numerical Weather Prediction
A1 Shirzadi, Navid
A1 Nasiri, Fuzhan
A1 Menon, Ramanunni Parakkal
A1 Monsalvete Álvarez de Uribarri, María Del Pilar
A1 Kaifel, Anton
A1 Eicker, U.
A2 Máquinas y Motores Térmicos
K1 wind porwer generation
K1 wind speed forecasting
K1 deep learning
K1 Weibull distribution
K1 numerical weather prediction
K1 smart cities
AB The design, operational planning, and integration of wind power plants with other renewables and the grid face challenges attributed to the intermittent nature of wind power generation. Addressing this issue necessitates the development of a smart wind power (and in particular wind speed) forecasting approach. This is a complex task due to substantial fluctuations in wind speed. To overcome the inherent stochastic nature of wind speed and mitigate related challenges, traditionally, numerical weather prediction (NWP) models are employed for wind speed forecasting. However, the applicability of NWP models is limited to short-term forecasting due to their computational constraints. In this study, a hybrid AI-based approach is proposed to improve forecast accuracy over a 48 h horizon for the city of Montreal. The results demonstrate that by integrating the probability distribution of wind speed with a deep learning model, the forecasted values align closely with the observed values in terms of seasonality and trend, exhibiting enhanced accuracy. Evaluation metrics reveal a substantial reduction in the root mean squared error (13–31%) across three prediction horizons (summer, fall, and winter) compared to a single long, short-term memory model. Furthermore, integrating the improved model with the numerical weather prediction model yields increased accuracy and decreased error compared to the LSTM–Weibull model.
PB MDPI
SN 1996-1073
YR 2023
FD 2023
LK http://hdl.handle.net/10498/36987
UL http://hdl.handle.net/10498/36987
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 22-sep-2026