Forecasting Gas Usage for Big Buildings using Generalized Additive Models and Deep Learning
| dc.contributor.author | Pathak, Nilavra | |
| dc.contributor.author | Ba, Amadou | |
| dc.contributor.author | Ploennings, Joern | |
| dc.contributor.author | Roy, Nirmalya | |
| dc.date.accessioned | 2018-08-03T12:36:09Z | |
| dc.date.available | 2018-08-03T12:36:09Z | |
| dc.date.issued | 2018 | |
| dc.description | 4th IEEE International Conference on Smart Computing (SmartComp), 2018 | en_US |
| dc.description.abstract | Time series behavior of gas consumption is highly irregular, non-stationary, and volatile due to its dependency on the weather, users’ habits and lifestyle. This complicates the modeling and forecasting of gas consumption with most of the existing time series modeling techniques, specifically when missing values and outliers are present. To demonstrate and overcome these problems, we investigate two approaches to model the gas consumption, namely Generalized Additive Models (GAM) and Long Short-Term Memory (LSTM). We perform our evaluations on two building datasets from two different conti-nents. We present each selected feature’s influence, the tuning parameters, and the characteristics of the gas consumption on their forecasting abilities. We compare the performances of GAM and LSTM with other state-of-the-art forecasting approaches. We show that LSTM outperforms GAM and other existing approaches, however, GAM provides better interpretable results for building management systems (BMS). | en_US |
| dc.description.uri | http://mpsc.umbc.edu/wp-content/uploads/2018/05/smartcomp18_nilavra.pdf | en_US |
| dc.format.extent | 8 pages | en_US |
| dc.genre | conference papers and proceedings preprints | en_US |
| dc.identifier | doi:10.13016/M2736M54R | |
| dc.identifier.uri | http://hdl.handle.net/11603/11040 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | IEEE | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Information Systems Department Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.relation.ispartof | UMBC Student Collection | |
| dc.rights | This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please contact the author. | |
| dc.subject | gas forecasting | en_US |
| dc.subject | deep learning | en_US |
| dc.subject | long short term memory | en_US |
| dc.subject | generalized additive model | en_US |
| dc.subject | forecasting | en_US |
| dc.subject | Time series behavior of gas consumption is highly irregular, non-stationary, and volatile due to its dependency on the weather, users’ habits and lifestyle. This complicates the modeling and forecasting of gas consumption with most of the existing time series modeling techniques, specifically when missing values and outliers are present. To demonstrate and overcome these problems, we investigate two approaches to model the gas consumption, namely Generalized Additive Models (GAM) and Long Short-Term Memory (LSTM). We perform our evaluations on two building datasets from two different conti-nents. We present each selected feature’s influence, the tuning parameters, and the characteristics of the gas consumption on their forecasting abilities. We compare the performances of GAM and LSTM with other state-of-the-art forecasting approaches. We show that LSTM outperforms GAM and other existing approaches, however, GAM provides better interpretable results for building management systems (BMS). | en_US |
| dc.title | Forecasting Gas Usage for Big Buildings using Generalized Additive Models and Deep Learning | en_US |
| dc.type | Text | en_US |