Abstract
The ability to precisely forecast power generation for large wind farms is very important, since such generation is highly unstable and creates problems for Distribution and Transmission System Operators to properly prepare the power system for operation. Forecasts for the next 24 h play an important role in this process. They are also used in energy market transactions. Even a small improvement in the quality of these forecasts translates into more security of the system and savings for the economy. Using two wind farms for statistical analyses and forecasting considerably increases credibility of newly created effective prediction methods and formulated conclusions. In the first part of our study, we have analysed the available data to identify potentially useful explanatory variables for forecasting models with additional development of new input data based on the basic data set. We demonstrate that it is better to use Numerical Weather Prediction (NWP) point forecasts for hourly lags: −3, 2, −1, 0, 1, 2, 3 (original contribution) as input data than lags 0, 1 that are typically used. Also, we prove that it is better to use forecasts from two NWP models as input data. Ensemble, hybrid and single methods are used for predictions, including machine learning (ML) solutions like Gradient-Boosted Trees (GBT), Random Forest (RF), Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), K-Nearest Neighbours Regression (KNNR) and Support Vector Regression (SVR). Original ensemble methods, developed for researching specific implementations, have reduced errors of forecast energy generation for both wind farms as compared to single methods. Predictions by the original ensemble forecasting method, called “Ensemble Averaging Without Extremes” have the lowest normalized mean absolute error (nMAE) among all tested methods. A new, original “Additional Expert Correction” additionally reduces errors of energy generation forecasts for both wind farms. The proposed ensemble methods are also applicable to short-time generation forecasting for other renewable energy sources (RES), e.g., hydropower or photovoltaic (PV) systems.
Highlights
The impact of humanity on climate change is a fact accepted by most scientists and policymakers
Perform extensive statistical analysis of time series of energy generated in two wind farms and perform statistical analysis of potential exogenous explanatory variables; Perform very extensive analysis of sensitivity of explanatory variables; Verify the accuracy of forecasts conducted by single methods, hybrid methods, and ensemble methods (13 methods in total); Develop and verify an original ensemble method, called “Ensemble Averaging Without Extremes” and conduct an original selection of combinations of predictors for ensemble methods; Identify the most efficient forecasting methods from among tested methods for data from both wind farms
For each input, the overall rating was calculated as the arithmetic mean of 4 results of global sensitivity analysis obtained from each Multi-Layer Perceptron (MLP) model
Summary
The impact of humanity on climate change is a fact accepted by most scientists and policymakers. The largest increases in energy production come from wind sources. They are known for their basic disadvantage, which is intermittent power generation. A way to overcome this drawback is to develop best possible energy production forecasts and properly prepare the power system for operation by Distribution and Transmission System Operators. Forecasts for the day play an important role in this process. They are used in energy market transactions. Models at large wind farms; conducting comparative analysis of forecast quality for various wind farms. No real-world wind speed data had been collected, which has made data analysis difficult
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