Implementing artificial neural networks in energy building applications — A review

Georgiou, Giorgos S.; Christodoulides, Paul; Kalogirou, Soteris A.

doi:10.1109/energycon.2018.8398847

Cited by 13 publications

(10 citation statements)

References 26 publications

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“…Some of the most frequently applied ANN approaches include Hopfield network, multi-layer perceptron, perceptron, and radial basis function networks (RBFN) [59]. The main advantages of ANN include: superior ability to solve non-linear problems that are associated with highly dimensional datasets, handling large and incomplete datasets (including those containing random noise) and self-adaption to dynamic scenarios [60]. Besides self-adaptation, self-organisation and real-time learning, basic ANN-based models are relatively easy to construct.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

“…The articles mainly iterated the advantages, disadvantages and application areas of the most common classes, with keen emphases on building types, temporal granularity of prediction, types of energy usage predictions and the characteristics of the training/testing data ( [13], [23], [27]). Additionally, 4 review articles ( [15], [20], [60], [103]) targeted ANN-based applications. A summary of their major findings revealed that Mohandes et al [103], Runge and Zmeureanu [20] investigated the practicability of ANNs in analysing issues related to building energy, while Georgiou et al [60] gave a brief review of the basic theory of ANNs as well as their specific applications to building energy management, systems control and energy prediction.…”

Section: Extraction Of Data and Synthesis Of Major Findings From Included Articlementioning

confidence: 99%

“…Additionally, 4 review articles ( [15], [20], [60], [103]) targeted ANN-based applications. A summary of their major findings revealed that Mohandes et al [103], Runge and Zmeureanu [20] investigated the practicability of ANNs in analysing issues related to building energy, while Georgiou et al [60] gave a brief review of the basic theory of ANNs as well as their specific applications to building energy management, systems control and energy prediction. Additionally, Ahmad and Hassan [15] reviewed the use of building electrical energy consumption prediction methods that were primarily based several artificial intelligence (AI) approaches especially ANN, SVM and their fusion.…”

Section: Extraction Of Data and Synthesis Of Major Findings From Included Articlementioning

confidence: 99%

See 2 more Smart Citations

Towards developing a systematic knowledge trend for building energy consumption prediction

Qiao

Yunusa‐Kaltungo

Edwards

2021

Journal of Building Engineering

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Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Section: Extraction Of Data and Synthesis Of Major Findings From Included Articlementioning

confidence: 99%

Section: Extraction Of Data and Synthesis Of Major Findings From Included Articlementioning

confidence: 99%

See 1 more Smart Citation

Towards developing a systematic knowledge trend for building energy consumption prediction

Qiao

Yunusa‐Kaltungo

Edwards

2021

Journal of Building Engineering

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“…The method described here is a deep, dilated convolutional network, a form of artificial neural network (ANN). ANNs have been applied in various applications relating to energy use in buildings [3] . ANNs were successfully used to predict indoor temperature [ 4 , 5 ] and applied to determine optimal heating start times in buildings [6] .…”

Section: Overviewmentioning

confidence: 99%

Inferring room-level use of domestic space heating from room temperature and humidity measurements using a deep, dilated convolutional network

2021

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Time series data about when heating is on and off in homes can be useful for research on building energy use and occupant behaviours, particularly data at room level and at a granularity of minutes. Direct methods which measure the temperature of radiators and other heaters can be effective at producing such data, but are expensive. Indirect methods, which infer heating on- and off-times from ambient room temperature data, can be cheaper but produce more error-prone data. Existing indirect methods have however utilised relatively simple prediction algorithms based on changes in ambient temperature between closely adjacent time points. In the method presented here we have implemented several refinements to this approach: An Artificial Neural Network algorithm is applied to the prediction task: a deep, dilated convolutional network. A wider range of input features is utilised to base predictions upon: ambient room temperature and humidity, and external temperature and humidity. Predictions for each time point are based on data from a wider, 600-minute, time window. We evaluate model performance on a dataset with 10 min granularity and achieve mean precision and recall during the heating season of >=0.74 for individual time points, and >=0.82 for full heating events, outperforming comparator methods.

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“…We used DDA and numerical tools to build the data set of the extinction spectrum of perfect and concave gold nanocubes. Also, we adopted three different ML methods, ridge regression, K-nearest neighbors (K-NN) regression, , and a multilayer perceptron neural network, to analyze the data set and study the relationships between the geometrical characteristics of the GCNCs and the wavelength of their dipole SPR. Finally, we compare the accuracy of the three ML methods for predicting the dipole SPR.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Predicting the Surface Plasmon Resonance of Perfect and Concave Gold Nanocubes

Arzola-Flores

González

2020

J. Phys. Chem. C

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Using the combination of the discrete dipole approximation (DDA) and machine learning methods, we have developed a computational tool to predict the wavelength at which the dipole surface plasmon resonance (SPR) of gold concave nanocubes (GCNCs) takes place. First, we have used the DDA to generate SPR data considering two main features, the length and the concavity of the nanocube. Then, for training, test, and validation, two mechanisms were considered. Mechanism A consisted in splitting 100% of the generated data into two separate sets, one covering 75% and other with the remaining 25% of the whole data. The two separate subsets were used as training and test sets, respectively. Mechanism B basically consisted of SPR data set splitting into k subsets, following a k-fold cross validation procedure. For the machine learning algorithms, we used the K-nearest neighbors model, the ridge regression, and the artificial neural network regressor. The three models utilized here lead to different accuracies that depend on the selection of the mechanism used, either A or B. It was found that the accuracy in the prediction is sensitive to the L 2 regularization when the ridge regression is used. On the other hand, when the K-nearest neighbors model is employed, the accuracy depends on the number of nearest neighbors utilized during the calculations. With ridge regression or Knearest neighbors, the accuracy obtained is between 80 and 94%. The best method for the SPR prediction of the GCNCs is the artificial neural network with eight neurons and L 2 = 10 because the accuracy for the predicted values is around 93%.

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Implementing artificial neural networks in energy building applications — A review

Cited by 13 publications

References 26 publications

Towards developing a systematic knowledge trend for building energy consumption prediction

Towards developing a systematic knowledge trend for building energy consumption prediction

Inferring room-level use of domestic space heating from room temperature and humidity measurements using a deep, dilated convolutional network

Machine Learning for Predicting the Surface Plasmon Resonance of Perfect and Concave Gold Nanocubes

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