Smart Urban Wind Power Forecasting: Integrating Weibull Distribution, Recurrent Neural Networks, and Numerical Weather Prediction

Abstract

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.

Review of AI-Based Wind Prediction within Recent Three Years: 2021–2023

“…Tree-based models encompass decision tree (DT), RF [86], gradient boosting decision tree (GBDT), gradient boosting regression tree (GBRT), extreme gradient boosting (XGBoost) [87], and light gradient boosting machine (LightGBM) [88]. With the advancement of DL technologies, deep neural networks (DNNs), including RNN [89], LSTM [90], BiLSTM [91], GRU [92], BiGRU, DBN, deep ELM (DELM), and Transformer have been widely applied in WSP and WPP due to their outstanding capability in handling complex nonlinear problems. Ding et al [90] used CEEMD to decompose the non-stationary wind power time series into a series of relatively stationary components.…”