Wind forecasting

This article uses Random Forest (RF) and k-fold cross-validation to predict the hourly count of rental bikes (cnt/h) in the city of Seoul (Korea) using information related to rental hour, temperature, humidity, wind speed, visibility, dewpoint, solar radiation, snowfall, and rainfall.

9p viengfa 28-10-2024 2 2   Download

We also applied our proposed model to a real life system of an islanded MG at small-scale with demand, PV power, and WTG power forecast, and the DEG output power is control variable. The results of test system have demonstrated the effectiveness of the proposed method.

9p viyoko 01-10-2024 5 2   Download

This study proposes an alternative approach based on the deep learning paradigm working in a complementary way with conventional methods such as the finite element method for quickly forecasting the responses of structures under random wind loads with reasonable accuracy.

11p vifaye 20-09-2024 3 1   Download

This paper proposes a procedural pipeline for wind forecasting based on clustering and regression. First, the data are clustered into groups sharing similar dynamic properties. Then, data in the same cluster are used to train the neural network that predicts wind speed. For clustering, a hidden Markov model (HMM) and the modified Bayesian information criteria (BIC) are incorporated in a new method of clustering time series data.

16p dieutringuyen 07-06-2017 37 2   Download