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| Creator | Title | Description | Subject | Date | ||
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Rahman, Aowabin | Deep recurrent neural networks for building energy prediction | This poster illustrates the development of a deep recurrent neural network (RNN) model using long-short-term memory (LSTM) cells to predict energy consumption in buildings at one-hour time resolution over medium-to-long term time horizons ( greater than or equal to 1 week). | Machine learning; Energy; Building energy modeling; Deep learning; Recurrent neural networks; Prediction | 2017-01-13 |
| 2 |
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Rahman, Aowabin | Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks | This paper presents a recurrent neural network model to make medium-to-long term predictions, i.e. time horizon of ≥ 1 week, of electricity consumption profiles in commercial and residential buildings at one-hour resolution. Residential and commercial buildings are responsible for a significant fr... | Building Energy Modeling; Machine learning; Recurrent neural networks; Deep learning; Electric load prediction | 2017 |
| 3 |
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Rahman, Aowabin | Predicting fuel consumption for commercial building with machine learning algorithms | This paper presents a modeling framework that uses machine learning algorithms to make longterm, i.e. one year-ahead predictions, of fuel consumption in multiple types of commercial prototype buildings at one-hour resolutions. Weather and schedule variables were used as model inputs, and the hourly ... | Building energy modeling; Machine learning; Prediction; Heating load; Data-driven modeling | 2017-08 |
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