# Deep-Learning Model Selection and Parameter Estimation from a Wind Power Farm in Taiwan +
## 1 Introduction  
Wind power forecasting techniques refer to estimates of the expected electrical output from one turbine or many wind turbines of a wind farm in the near future, up to one year. The prediction method for wind power forecasting involves performing model pre-training and power prediction by collecting meteorological information and wind power generation historical data. Meteorological historical data include temperature, humidity, average wind speed, and wind direction. Conventionally, meteorological engineers use physical methods for wind forecasting associated with numerical weather prediction (NWP) to identify weather changes. Treating weather changes as deterministic events is not a trivial task. In fact, it requires that meteorological engineers handle numerical weather data updates for immediate weather changes. Basically, forecasting of wind power generation becomes imprecise when the NWP results are poor. Moreover, current statistical schemes, such asautoregressive (AR) and autoregressive integrated moving average (ARIMA) [1], have been proposed to determine the relationship between weather features and predicted power, where weather changes are considered random processes. Statistical models generally perform well in simple time series forecasting, but their ability to process nonlinear data in dynamic states remains insufficient [2], and the fitting performance is limited. Recently, numerous machine learning (ML) algorithms [3; 4; 5] have been proposed to help engineers distinguish the correlations between climate features and wind power outputs by accumulating historical meteorological information and wind power generation data. For example, artificial neural networks (ANNs), genetic algorithms, fuzzy regression, cluster analysis (K-means), and support vector machines have been introduced to solve the problems of wind-power forecasting in climate change. However, traditional ML models for predicting the accuracy of wind-power forecasting have challenges with some problems, including, long-term stable prediction under climate change, as their complex learning architecture may result in low efficiency, long training time, and under-fitting [6]. Thus, developing a precise and robust approach for forecasting a wide range of meteorological phenomena with wind power forecasting remains a challenge. To address the aforementioned problems in model training that often occur in the thin-layer network architecture of ML approaches, deep learning network (DLN) approaches use multilayer neural networks to extract the more complex climate features from historical weather information that accurately assist engineers in predicting the power generation (kw/h) for wind turbines. Typical deep learning models for sequence-to-sequence data include recurrent neural networks (RNN) [7], long short-term memory (LSTM) [8; 9], gated recurrent units (GRU) [10], and temporal convolution networks (TCN) [11; 12; 13]. These four models have been used as learning models for the DLN to establish a classifier after their architecture hyperparameters and features are considered. Practically, when attempting to build a DLN model, there are several questions that should be answered: (1) What is the best model for the wind power prediction problem to be solved? (2) How to select hyperparameters associated with the model? Hyperparameters include model architecture-related parameters, such as the size of hidden layers. Evidently, one should decide training-related parameters, such as activation function, batch size, the learning rate, and stopping time, given the alternatives of the optimizers, such as RMSprop, SDG, and Adam [14]. We attempt to answer these questions in this study and achieve high prediction precision with low convergence errors of the cost function in the predicted output. To answer the first question, there are a number of options with regard to DLN model choice; for example, if the input data form a sequence with dependencies between the elements of the sequence, then an RNN is required.