Affiliation:
1. Department of Industrial Engineering, CITIC; University of A Coruña; CTC, Avda. 19 de Febrero s/n; 15405; Ferrol, A Coruña, Spain
Abstract
Abstract
In this study, a hybrid model based on intelligent techniques is developed to predict the active power generated in a bioclimatic house by a low power wind turbine. Contrary to other researches that predict the generated power taking into account the speed and the direction of the wind, the model developed in this paper only uses the speed of the wind, measured mainly in a weather station from the government meteorological agency (MeteoGalicia). The wind speed is measured at different heights, against the usual measurements in others researches, which uses the wind speed and the direction measured in a weather station on the wind turbine nacelle. The prediction is performed 30 minutes ahead, what ensures that the Building Management System knows the energy generated by the low power wind turbine 30 minutes before, and it can adapt the consumption of different equipment in the house to optimize the power use. The main objective is to allow the Building Management System to optimize the uses of energy, taking into account the predicted amount of energy that will be produced and the energy consumed in the house. The developed model uses a hybrid topology with four clusters to improve the prediction, achieving an error lower than 6.5% for Mean Absolute Error measured in a final test. To perform this test, part of the original dataset was isolated from the beginning of the training process to check the model with a dataset that is not used before, simulating the model as it is receiving new data.
Publisher
Oxford University Press (OUP)
Cited by
10 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Machine Learning Based System for Detecting Battery State-of-Health;18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023);2023
2. Comparative Study of Wastewater Treatment Plant Feature Selection for COD Prediction;18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023);2023
3. Comparative Study of Regression Models Applied to the Prediction of Energy Generated by a Micro Wind Turbine;18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023);2023
4. System Identification and Emulation of a Physical Level Control Plant Using a Low Cost Embedded System;Lecture Notes in Networks and Systems;2023
5. Comparative Study of Forecasting Techniques for Small Wind Turbine Power Generation by Meteorological Parameters;Distributed Computing and Artificial Intelligence, Special Sessions II - Intelligent Systems Applications, 20th International Conference;2023