Estimating building energy consumption using extreme learning machine method

Naji, Sareh; Keivani, Afram; Shamshirband, Shahaboddin; Alengaram, U. Johnson; Jumaat, Mohd Zamin; Mansor, Zulkefli; Lee, Malrey

doi:10.1016/j.energy.2015.11.037

Cited by 169 publications

(52 citation statements)

References 22 publications

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“…Buildings are identified as a major energy consumer worldwide, accounting for 20%-40% of the total energy production [1]- [3]. In addition to being a major energy consumer, buildings are shown to account for a significant portion of energy wastage as well [4]. As energy wastage poses a threat to sustainability, making buildings energy efficient is extremely crucial.…”

Section: Introductionmentioning

confidence: 99%

Building energy load forecasting using Deep Neural Networks

Marino¹,

Amarasinghe²,

Manic³

2016

IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society

458

197

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Ensuring sustainability demands more efficient energy management with minimized energy wastage. Therefore, the power grid of the future should provide an unprecedented level of flexibility in energy management. To that end, intelligent decision making requires accurate predictions of future energy demand/load, both at aggregate and individual site level. Thus, energy load forecasting have received increased attention in the recent past, however has proven to be a difficult problem. This paper presents a novel energy load forecasting methodology based on Deep Neural Networks, specifically Long Short Term Memory (LSTM) algorithms. The presented work investigates two variants of the LSTM: 1) standard LSTM and 2) LSTM-based Sequence to Sequence (S2S) architecture. Both methods were implemented on a benchmark data set of electricity consumption data from one residential customer. Both architectures where trained and tested on one hour and one-minute time-step resolution datasets. Experimental results showed that the standard LSTM failed at one-minute resolution data while performing well in one-hour resolution data. It was shown that S2S architecture performed well on both datasets. Further, it was shown that the presented methods produced comparable results with the other deep learning methods for energy forecasting in literature.

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Section: Introductionmentioning

confidence: 99%

Building energy load forecasting using Deep Neural Networks

Marino¹,

Amarasinghe²,

Manic³

2016

IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society

458

197

View full text Add to dashboard Cite

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“…The use of ANNs for general load forecasting has been explored in several studies, for all three forecasting horizons: short, medium and long [18] [19]. In comparison, the work by Naji et al [20] predicted building energy consumption by applying an Extreme Learning Machine method with the data regarding building material thickness and their thermal insulation capability. Several studies [21] [22] have proposed ANN and SVM models for estimating energy consumption and compared performance.…”

Section: Related Workmentioning

confidence: 99%

Forecasting Building Energy Consumption with Deep Learning: A Sequence to Sequence Approach

Sehovac

Nesen

Grolinger

2019

2019 IEEE International Congress on Internet of Things (ICIOT)

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Energy Consumption has been continuously increasing due to the rapid expansion of high-density cities, and growth in the industrial and commercial sectors. To reduce the negative impact on the environment and improve sustainability, it is crucial to efficiently manage energy consumption. Internet of Things (IoT) devices, including widely used smart meters, have created possibilities for energy monitoring as well as for sensor based energy forecasting. Machine learning algorithms commonly used for energy forecasting such as feedforward neural networks are not well-suited for interpreting the time dimensionality of a signal. Consequently, this paper uses Recurrent Neural Networks (RNN) to capture time dependencies and proposes a novel energy load forecasting methodology based on sample generation and Sequence-to-Sequence (S2S) deep learning algorithm. The S2S architecture that is commonly used for language translation was adapted for energy load forecasting. Experiments focus on Gated Recurrent Unit (GRU) based S2S models and Long Short-Term Memory (LSTM) based S2S models. All models were trained and tested on one building-level electrical consumption dataset, with five-minute incremental data. Results showed that, on average, the GRU S2S models outperformed LSTM S2S, RNN S2S, and Deep Neural Network models, for short, medium, and long-term forecasting lengths.

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“…In [52], data-driven models were contrasted for predicting retrofit energy savings, in building retrofit projects. In [53], machine learning was adopted to predict energy consumption based on insulation thickness and envelop materials of a building.…”

Section: Ai In Buildingsmentioning

confidence: 99%

Minimising the Deviation between Predicted and Actual Building Performance via Use of Neural Networks and BIM

Hammad

2019

Buildings

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Building energy performance tools are widely used to simulate the expected energy consumption of a given building during the operation phase of its life cycle. Deviations between predicted and actual energy consumptions have however been reported as a major limiting factor to the tools adopted in the literature. A significant reason highlighted as greatly influencing the difference in energy performance is related to the occupant behaviour of the building. To enhance the effectiveness of building energy performance tools, this study proposes a method which integrates Building Information Modelling (BIM) with artificial neural network model for limiting the deviation between predicted and actual energy consumption rates. Through training a deep neural network for predicting occupant behaviour that reflects the actual performance of the building under examination, accurate BIM representations are produced which are validated via energy simulations. The proposed method is applied to a realistic case study, which highlights significant improvements when contrasted with a static simulation that does not account for changes in occupant behaviour.

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Estimating building energy consumption using extreme learning machine method

Cited by 169 publications

References 22 publications

Building energy load forecasting using Deep Neural Networks

Building energy load forecasting using Deep Neural Networks

Forecasting Building Energy Consumption with Deep Learning: A Sequence to Sequence Approach

Minimising the Deviation between Predicted and Actual Building Performance via Use of Neural Networks and BIM

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