Probabilistic short term wind power forecasts using deep neural networks with discrete target classes

Abstract

Abstract. Usually, neural networks trained on historical feed-in time series of wind turbines deterministically predict power output over the next hours to days. Here, the training goal is to minimise a scalar cost function, often the root mean square error (RMSE) between network output and target values. Yet similar to the analog ensemble (AnEn) method, the training algorithm can also be adapted to analyse the uncertainty of the power output from the spread of possible targets found in the historical data for a certain meteorological situation. In this study, the uncertainty estimate is achieved by discretising the continuous time series of power targets into several bins (classes). For each forecast horizon, a neural network then predicts the probability of power output falling into each of the bins, resulting in an empirical probability distribution. Similiar to the AnEn method, the proposed method avoids the use of costly numerical weather prediction (NWP) ensemble runs, although a selection of several deterministic NWP forecasts as input is helpful. Using state-of-the-art deep learning technology, we applied our method to a large region and a single wind farm. MAE scores of the 50-percentile were on par with or better than comparable deterministic forecasts. The corresponding Continuous Ranked Probability Score (CRPS) was even lower. Future work will investigate the overdispersiveness sometimes observed, and extend the method to solar power forecasts.

Optimization of the ANNs Models Performance in the Short-Term Forecasting of the Wind Power of Wind Farms

“…To analyze the results, the mean absolute percentage error (MAPE), normalized mean absolute error (NMAE) and normalized root mean square error (NRMSE) metrics are used, calculated as the mean value in the forecasting horizons (24 h and 6 h). As in [34], models have been developed for different forecasting horizons [26,27,33]. However, an extensive analysis of the literature conducted by the authors of the present study has found that the models developed to date only consider a specific and fixed number of prior 1-h periods (periods prior to the prediction hour).…”

“…In most cases, good performances for specific forecasting horizons have been obtained. The techniques that have been used range from simple heuristics [14][15][16][17][18][19][20] to systems which employ artificial intelligence [21][22][23][24][25][26][27][28][29][30][31][32][33][34].…”

Performance Improvement of Artificial Neural Network Model in Short-term Forecasting of Wind Farm Power Output

“…The techniques range from simple heuristics [13] - [17] to artificial intelligence [18] - [29]. This paper focuses on models which employ the technique of artificial neural networks (ANNs) to forecast wind power production [21], [22], [24]- [26], [28], [29].…”

“…To analyse the results, the mean absolute percentage error (MAPE), normalized mean absolute error (NMAE) and normalized root mean square error (NRMSE) metrics are used, calculated as the mean value in the forecasting horizons (24 hours and 6 hours). As in [29], models have been developed for different forecasting horizons [21], [22], [28]. However, an extensive analysis of the literature has found that the models developed to date only consider a specific and fixed number of prior 1-h periods (periods prior to the forecasting hour).…”

Power Optimization for Wind Turbines Based on Stacking Model and Pitch Angle Adjustment

“…In fact, many papers related to power prediction for wind turbines have been published, like [14], where researchers created a turbine regression tree model to predict power output. In [15], authors used deep neural networks with discrete target classes to do probabilistic short-term wind power forecasts. Besides, BP and RBF neural networks were applied to realize wind power short-term prediction in [16].…”