Deep learning for estimating building energy consumption

By Elena Mocanu, H. Nguyen, Madeleine Gibescu, W. Kling. 2016

587 citations recorded by the source corpus.

Subfields: Building and Construction, Electrical and Electronic Engineering

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In Building and Construction

In Electrical and Electronic Engineering

Citations in the corpus

Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, methodology): “In [122], two variations of RBMs are used to forecast energy consumption for short term intervals in residential houses.”
Building energy load forecasting using Deep Neural Networks (2016, methodology): “At the grid level, to minimizing the energy wastage and making the power generation and distribution more efficient, the future of the power grid is moving to a new paradigm of smart grids [5], [6].”
Deep Learning for Time Series Forecasting: The Electric Load Case (2019, methodology): “…techniques ranging from advanced feed forward architecture with residual connections [14] to convolutional approaches [15, 16] and Recurrent Neural Networks [17,18] along with their many variants such as Echo-state Network [18–20], Long-Short Term Memory [18,21–23] and Gated Recurrent Unit [18,24].”
Big IoT data mining for real-time energy disaggregation in buildings (2016, methodology): “Inspired by the successful application of Deep Learning (DL) methods to the building energy prediction [25], [26] problem, in this paper, our proposed approach goes beyond the state-of-the-art in the ongoing research on demand flexibility methods.”
Intelligent Buildings of the Future: Cyberaware, Deep Learning Powered, and Human Interacting (2016, methodology): “The authors concluded that the FCRBM outperforms all the other methods.”
A review of the-state-of-the-art in data-driven approaches for building energy prediction (2020, background): “[133] illustrates that under Strategy 3, one-day ahead energy prediction is worse than one hour ahead prediction with 1-minute resolution data.”
Deep neural network based demand side short term load forecasting (2016, methodology): “For estimating building energy consumption, conditional restricted Boltzmann machine and factored conditional restricted Boltzmann machine are used in [7], and deep belief network (DBN) based reinforcement learning forecasting model is developed in [8].”
Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges (2018, methodology): “Conditional Restricted Boltzmann Machine (FCRBM) were assessed in [113] to estimate energy consumption for a house-”
Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches † (2018, methodology): “Recently, in 2016, Mocanu et al. [1] employed, a deep learning approach based on a restricted Boltzmann machine, for single-meter residential load forecasting.”
Deep neural networks for energy load forecasting (2017, methodology): “In terms of demand response, individual component/ building level demand forecasting enables responding to local demand with local generations [2].”
Predicting residential energy consumption using CNN-LSTM neural networks (2019, methodology): “Mocanu et al. investigated two newly developed stochastic models for time series prediction of energy consumption, namely conditional restricted Boltzmann machine (CRBM) and factored conditional restricted Boltzmann machine (FCRBM) [47].”