An Analysis of Short-Term Load Forecasting on Residential Buildings Using Deep Learning Models
| dc.contributor.author | Suresh, Sreerag | en |
| dc.contributor.committeechair | Jazizadeh, Farrokh | en |
| dc.contributor.committeemember | Marr, Linsey C. | en |
| dc.contributor.committeemember | Isaacman-VanWertz, Gabriel | en |
| dc.contributor.department | Environmental Science and Engineering | en |
| dc.date.accessioned | 2020-07-08T08:00:42Z | en |
| dc.date.available | 2020-07-08T08:00:42Z | en |
| dc.date.issued | 2020-07-07 | en |
| dc.description.abstract | Building energy load forecasting is becoming an increasingly important task with the rapid deployment of smart homes, integration of renewables into the grid and the advent of decentralized energy systems. Residential load forecasting has been a challenging task since the residential load is highly stochastic. Deep learning models have showed tremendous promise in the fields of time-series and sequential data and have been successfully used in the field of short-term load forecasting at the building level. Although, other studies have looked at using deep learning models for building energy forecasting, most of those studies have looked at limited number of homes or an aggregate load of a collection of homes. This study aims to address this gap and serve as an investigation on selecting the better deep learning model architecture for short term load forecasting on 3 communities of residential buildings. The deep learning models CNN and LSTM have been used in the study. For 15-min ahead forecasting for a collection of homes it was found that homes with a higher variance were better predicted by using CNN models and LSTM showed better performance for homes with lower variances. The effect of adding weather variables on 24-hour ahead forecasting was studied and it was observed that adding weather parameters did not show an improvement in forecasting performance. In all the homes, deep learning models are shown to outperform the simple ANN model. | en |
| dc.description.abstractgeneral | Building energy load forecasting is becoming an increasingly important task with the rapid deployment of smart homes, integration of renewables into the grid and the advent of decentralized energy systems. Residential load forecasting has been a challenging task since residential load is highly stochastic. Deep learning models have showed tremendous promise in the fields of time-series and sequential data and have been successfully used in the field of short-term load forecasting. Although, other studies have looked at using deep learning models for building energy forecasting, most of those studies have looked at only a single home or an aggregate load of a collection of homes. This study aims to address this gap and serve as an analysis on short term load forecasting on 3 communities of residential buildings. Detailed analysis on the model performances across all homes have been studied. Deep learning models have been used in this study and their efficacy is measured compared to a simple ANN model. | en |
| dc.description.degree | Master of Science | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:26769 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/99287 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | load forecasting | en |
| dc.subject | building energy | en |
| dc.subject | CNN | en |
| dc.subject | deep learning | en |
| dc.subject | LSTM | en |
| dc.title | An Analysis of Short-Term Load Forecasting on Residential Buildings Using Deep Learning Models | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Environmental Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | Master of Science | en |
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