Back to Search
Start Over
Hybrid and Ensemble Methods of Two Days Ahead Forecasts of Electric Energy Production in a Small Wind Turbine
- Source :
- Energies, Vol 14, Iss 1225, p 1225 (2021), Energies; Volume 14; Issue 5; Pages: 1225
- Publication Year :
- 2021
- Publisher :
- MDPI AG, 2021.
-
Abstract
- The ability to forecast electricity generation for a small wind turbine is important both on a larger scale where there are many such turbines (because it creates problems for networks managed by distribution system operators) and for prosumers to allow current energy consumption planning. It is also important for owners of small energy systems in order to optimize the use of various energy sources and facilitate energy storage. The research presented here addresses an original, rarely predicted 48 h forecasting horizon for small wind turbines. This topic has been rather underrepresented in research, especially in comparison with forecasts for large wind farms. Wind speed forecasts with a 48 h horizon are also rarely used as input data. We have analyzed the available data to identify potentially useful explanatory variables for forecasting models. Eight sets with increasing data amounts were created to analyze the influence of the types and amounts of data on forecast quality. Hybrid, ensemble and single methods are used for predictions, including machine learning (ML) solutions like long short-term memory (LSTM), multi-layer perceptron (MLP), support vector regression (SVR) and K-nearest neighbours regression (KNNR). Original hybrid methods, developed for research of specific implementations and ensemble methods based on hybrid methods’ decreased errors of energy generation forecasts for small wind turbines in comparison with single methods. The “artificial neural network (ANN) type MLP as an integrator of ensemble based on hybrid methods” ensemble forecasting method incorporates an original combination of predictors. Predictions by this method have the lowest mean absolute error (MAE). In addition, this paper presents an original ensemble forecasting method, called “averaging ensemble based on hybrid methods without extreme forecasts”. Predictions by this method have the lowest root mean square error (RMSE) error among all tested methods. LSTM, a deep neural network, is the best single method, MLP is the second best one, while SVR, KNNR and, especially, linear regression (LR) perform less well. We prove that lagged values of forecasted time series slightly increase the accuracy of predictions. The same applies to seasonal and daily variability markers. Our studies have also demonstrated that using the full set of available input data and the best proposed hybrid and ensemble methods yield the lowest error. The proposed hybrid and ensemble methods are also applicable to other short-time generation forecasting in renewable energy sources (RES), e.g., in photovoltaic (PV) systems or hydropower.
- Subjects :
- Mathematical optimization
Control and Optimization
Small wind turbine
010504 meteorology & atmospheric sciences
Computer science
020209 energy
Energy Engineering and Power Technology
02 engineering and technology
lcsh:Technology
01 natural sciences
Wind speed
Energy storage
wind turbine
Electric energy
short-term forecasting
wind energy
0202 electrical engineering, electronic engineering, information engineering
Electrical and Electronic Engineering
Engineering (miscellaneous)
Hydropower
0105 earth and related environmental sciences
Wind power
Ensemble forecasting
lcsh:T
Renewable Energy, Sustainability and the Environment
business.industry
ensemble methods
Photovoltaic system
Perceptron
Ensemble learning
Renewable energy
hybrid methods
electric energy production
machine learning
deep neural network
swarm intelligence
Electricity generation
business
Energy source
Energy (miscellaneous)
Subjects
Details
- ISSN :
- 19961073
- Volume :
- 14
- Database :
- OpenAIRE
- Journal :
- Energies
- Accession number :
- edsair.doi.dedup.....343b6af6d4dbf23fd336723b95c05b3b