On-line building energy prediction using adaptive artificial neural networks

Yang, Jin Kyu; Rivard, Hugues; Zmeureanu, Radu

doi:10.1016/j.enbuild.2005.02.005

Cited by 299 publications

(112 citation statements)

References 11 publications

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“…Rather than using a static approach for the prediction of energy usage in buildings, Yang et al [16] used accumulative training and sliding window training to perform on-line building energy consumption. Yalcintas [17] trained an ANN using energy consumption data of the pre-retrofit period of a building; hence, the model can be used to predict the energy usage of the pre-retrofit equipment in the post-retrofit period.…”

Section: Artificial Neural Network In Energy Systems Applicationsmentioning

confidence: 99%

Diagnostic tools of energy performance for supermarkets using Artificial Neural Network algorithms

Mavromatidis

Acha

Shah

2013

Energy and Buildings

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Section: Artificial Neural Network In Energy Systems Applicationsmentioning

confidence: 99%

Diagnostic tools of energy performance for supermarkets using Artificial Neural Network algorithms

Mavromatidis

Acha

Shah

2013

Energy and Buildings

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“…ANNs have the ability to model linear and non-linear systems without the need to make assumptions implicitly as in most traditional statistical approaches. They have been applied in various aspects of science and engineering (Rivard & Zmeureanu, 2005;Chantasut et al, 2005). ANNs can be grouped into two major categories: feed-forward and feedback (recurrent) networks.…”

Section: Neural Networkmentioning

confidence: 99%

Using MATLAB to Develop Artificial Neural Network Models for Predicting Global Solar Radiation in Al Ain City – UAE

Shamisi¹,

Assi²,

Hejase³

2011

Engineering Education and Research Using MATLAB

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“…In [21,22], the ANN combined with a fuzzy inference system was examined by the building energy consumption prediction. In [23], two adaptive ANNs with accumulative training and sliding window training were proposed for real-time online building energy prediction. In [24], an ANN trained by the extreme learning machine (ELM) was proposed to estimate the building energy consumption and was compared with the genetic algorithm (GA)-based ANN.…”

Section: Introductionmentioning

confidence: 99%

Building Energy Consumption Prediction: An Extreme Deep Learning Approach

et al. 2017

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Building energy consumption prediction plays an important role in improving the energy utilization rate through helping building managers to make better decisions. However, as a result of randomness and noisy disturbance, it is not an easy task to realize accurate prediction of the building energy consumption. In order to obtain better building energy consumption prediction accuracy, an extreme deep learning approach is presented in this paper. The proposed approach combines stacked autoencoders (SAEs) with the extreme learning machine (ELM) to take advantage of their respective characteristics. In this proposed approach, the SAE is used to extract the building energy consumption features, while the ELM is utilized as a predictor to obtain accurate prediction results. To determine the input variables of the extreme deep learning model, the partial autocorrelation analysis method is adopted. Additionally, in order to examine the performances of the proposed approach, it is compared with some popular machine learning methods, such as the backward propagation neural network (BPNN), support vector regression (SVR), the generalized radial basis function neural network (GRBFNN) and multiple linear regression (MLR). Experimental results demonstrate that the proposed method has the best prediction performance in different cases of the building energy consumption.

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On-line building energy prediction using adaptive artificial neural networks

Cited by 299 publications

References 11 publications

Diagnostic tools of energy performance for supermarkets using Artificial Neural Network algorithms

Diagnostic tools of energy performance for supermarkets using Artificial Neural Network algorithms

Using MATLAB to Develop Artificial Neural Network Models for Predicting Global Solar Radiation in Al Ain City – UAE

Building Energy Consumption Prediction: An Extreme Deep Learning Approach

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