Optimizing Power Forecasting Models with Customized Features for Academic and Industrial Buildings
Power consumption prediction is a crucial component in enhancing the efficiency and sustainability of building operations. This study investigates the impact of data collection frequency and model selection on the predictive accuracy of power consumption in two distinct building types: an Academic o...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Smart Cities |
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| Online Access: | https://www.mdpi.com/2624-6511/8/1/3 |
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| author | David Cabezuelo Izar Lopez-Ramirez June Urkizu Ander Goikoetxea |
| author_facet | David Cabezuelo Izar Lopez-Ramirez June Urkizu Ander Goikoetxea |
| author_sort | David Cabezuelo |
| collection | DOAJ |
| description | Power consumption prediction is a crucial component in enhancing the efficiency and sustainability of building operations. This study investigates the impact of data collection frequency and model selection on the predictive accuracy of power consumption in two distinct building types: an Academic one with 15-min interval data and an Industrial one with hourly data. Various machine learning models, including Support Vector Machine (SVM) with Radial and Sigmoid kernels, Random Forest (RF), and Deep Neural Networks (DNNs), across different data splits and feature sets, were considered. Our analysis reveals that higher data collection frequency generally improves model performance, as indicated by lower RMSE, MAPE, and CV values, alongside higher R² scores. The inclusion of more historical power consumption features was also found to have a more significant impact on the accuracy of predictions than including climate condition features. Moreover, the SVM-Radial model consistently outperformed others, particularly in capturing complex, non-linear patterns in the data. However, the DNN model, while competent in some metrics, showed elevated MAPE values, suggesting potential overfitting issues. These findings suggest that careful consideration of data frequency, features, and model selection is essential for optimizing power prediction, contributing to more efficient power management strategies in building operations. |
| format | Article |
| id | doaj-art-4fca95a9ba11486db50077bc8466cfd4 |
| institution | DOAJ |
| issn | 2624-6511 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Smart Cities |
| spelling | doaj-art-4fca95a9ba11486db50077bc8466cfd42025-08-20T03:11:22ZengMDPI AGSmart Cities2624-65112024-12-0181310.3390/smartcities8010003Optimizing Power Forecasting Models with Customized Features for Academic and Industrial BuildingsDavid Cabezuelo0Izar Lopez-Ramirez1June Urkizu2Ander Goikoetxea3Computer and Electronics Department, Mondragon University, 20500 Mondragon, SpainComputer and Electronics Department, Mondragon University, 20500 Mondragon, SpainComputer and Electronics Department, Mondragon University, 20500 Mondragon, SpainComputer and Electronics Department, Mondragon University, 20500 Mondragon, SpainPower consumption prediction is a crucial component in enhancing the efficiency and sustainability of building operations. This study investigates the impact of data collection frequency and model selection on the predictive accuracy of power consumption in two distinct building types: an Academic one with 15-min interval data and an Industrial one with hourly data. Various machine learning models, including Support Vector Machine (SVM) with Radial and Sigmoid kernels, Random Forest (RF), and Deep Neural Networks (DNNs), across different data splits and feature sets, were considered. Our analysis reveals that higher data collection frequency generally improves model performance, as indicated by lower RMSE, MAPE, and CV values, alongside higher R² scores. The inclusion of more historical power consumption features was also found to have a more significant impact on the accuracy of predictions than including climate condition features. Moreover, the SVM-Radial model consistently outperformed others, particularly in capturing complex, non-linear patterns in the data. However, the DNN model, while competent in some metrics, showed elevated MAPE values, suggesting potential overfitting issues. These findings suggest that careful consideration of data frequency, features, and model selection is essential for optimizing power prediction, contributing to more efficient power management strategies in building operations.https://www.mdpi.com/2624-6511/8/1/3power consumption predictionmachine learning modelspredictive analyticsfeature analysisrandom forest (RF)support vector machine (SVM) |
| spellingShingle | David Cabezuelo Izar Lopez-Ramirez June Urkizu Ander Goikoetxea Optimizing Power Forecasting Models with Customized Features for Academic and Industrial Buildings Smart Cities power consumption prediction machine learning models predictive analytics feature analysis random forest (RF) support vector machine (SVM) |
| title | Optimizing Power Forecasting Models with Customized Features for Academic and Industrial Buildings |
| title_full | Optimizing Power Forecasting Models with Customized Features for Academic and Industrial Buildings |
| title_fullStr | Optimizing Power Forecasting Models with Customized Features for Academic and Industrial Buildings |
| title_full_unstemmed | Optimizing Power Forecasting Models with Customized Features for Academic and Industrial Buildings |
| title_short | Optimizing Power Forecasting Models with Customized Features for Academic and Industrial Buildings |
| title_sort | optimizing power forecasting models with customized features for academic and industrial buildings |
| topic | power consumption prediction machine learning models predictive analytics feature analysis random forest (RF) support vector machine (SVM) |
| url | https://www.mdpi.com/2624-6511/8/1/3 |
| work_keys_str_mv | AT davidcabezuelo optimizingpowerforecastingmodelswithcustomizedfeaturesforacademicandindustrialbuildings AT izarlopezramirez optimizingpowerforecastingmodelswithcustomizedfeaturesforacademicandindustrialbuildings AT juneurkizu optimizingpowerforecastingmodelswithcustomizedfeaturesforacademicandindustrialbuildings AT andergoikoetxea optimizingpowerforecastingmodelswithcustomizedfeaturesforacademicandindustrialbuildings |