Occupancy prediction through Markov based feedback recurrent neural network (M-FRNN) algorithm with WiFi probe technology

Wang, Wei; Chen, Jiayu; Hong, Tianzhen; Zhu, Na

doi:10.1016/j.buildenv.2018.04.034

Cited by 109 publications

(54 citation statements)

References 56 publications

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“…With the Bayesian network algorithm, they reported an occupancy status estimation accuracy of 96.7%. Wang et al [21,22] used Wi-Fi probe data, indoor environmental measurement data, and a camera for ground truth from an open office space. To estimate the number of occupants, a M-FRNN (Markov based feedback recurrent neural network) algorithm was suggested, and then compared with many other algorithms, such as ANN, kNN, and SVM.…”

Section: Necessity and Purpose Of Studymentioning

confidence: 99%

Development of a Consecutive Occupancy Estimation Framework for Improving the Energy Demand Prediction Performance of Building Energy Modeling Tools

et al. 2019

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To improve the energy prediction performance of a building energy model, the occupancy status information is very important. This is more important in real buildings, rather than under construction buildings, because actual building occupancy can significantly influence its energy consumption. In this study, a machine learning based framework for a consecutive occupancy estimation is proposed by utilizing internet of things data, such as indoor temperature and luminance, CO2 density, electricity consumption of lighting, HVAC (heating, ventilation, and air conditioning), electric appliances, etc. Three machine learning based occupancy estimation algorithms (decision tree, support vector machine, artificial neural networks) are selected and evaluated in terms of the performance of estimating the occupancy status for each season. The selection process of the input variables that have crucial impact on the algorithms’ performance are described in detail. Finally, an occupancy estimation framework that can repeat model training and estimation consecutively in a situation when time-series data are continuously provided over the entire measurement period is suggested. In addition, the performance of the framework is evaluated to identify how it improves the energy prediction performance of the building energy model compared to conventional energy modeling practices. The suggested framework is distinguished from similar previous studies in two ways: 1) The proposed framework reveals that input variables for the occupancy estimation model can be occasionally changed by an occupant response to certain times and seasons, and 2) the framework incorporates time-series indirect occupancy sensing data and classification algorithms to consecutively provide occupancy information for the energy modeling effort.

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Section: Necessity and Purpose Of Studymentioning

confidence: 99%

Development of a Consecutive Occupancy Estimation Framework for Improving the Energy Demand Prediction Performance of Building Energy Modeling Tools

et al. 2019

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“…To capture this time dependency, the Recurrent Neural Network (RNN) has been proposed, taking both the input at the current timestamp t and the state of the previous timestamp t-1 into consideration to predict the output h t , as illustrated in Figure 7(b). Because of the ability to capture the time-dependencies of time-series data, RNN has been applied to a variety of problems: speech recognition, translation, the prediction of occupancy [46], etc. RNN is powerful but not enough when the prediction tasks have long-term dependencies, which means the output of timestamp t, was not only influenced by the state of the previous timestamp t-1, but also by the state of k timestamps ago tk.…”

Section: From Ann To Lstmsmentioning

confidence: 99%

Predicting plug loads with occupant count data through a deep learning approach

Wang

Hong

Piette

2019

Energy

Self Cite

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Predictive control has gained increasing attention for its ability to reduce energy consumption and improve occupant comfort in buildings. The plug loads prediction is a key component for the predictive building controls, as plug loads is a major source of internal heat gains in buildings. This study proposed a novel method to apply the Long-Short-Term-Memory (LSTM) Network, a special form of Recurrent Neural Network, to predict plug loads. The occupant count and the time have been confirmed to drive the plug load profile and thus selected as the features for the plug load prediction. The LSTM network was trained and tested with ground truth occupant count data collected from a real office building in Berkeley, California. Results from the LSTM network markedly improve the prediction accuracy compared with traditional linear regression methods and the classical Artificial Neural Network. 95% of 1-hour predictions from LSTM network are within ±1 kW of the actual plug loads, given the average plug loads during the office hour is 8.6 kW. The CV(RMSE) of the predicted plug load is 11% for the next hour, and 20% for the next eight hours. Lastly, we compared four prediction approaches with the office building we monitored: LSTM vs. ARIMA, with occupant counts vs. without occupant counts. It was found, the prediction error of the LSTM approach is around 4% less than the ARIMA approach. Using occupant counts as an exogenous input could further reduce the prediction error by 5%-6%. The findings of this paper could shed light on the plug load prediction for building control optimizations such as model-predictive control.

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“…This kind of dropout technique reduces the number of cycles and computational time in the network. This kind of internal feedback structures will help to reduce the number of iterations for training and reduce overfitting [16].…”

Section: Fig 3: Basic Feedback Loop Connectionmentioning

confidence: 99%

Feedback Based Adaptive Recurrent Neural Network for Cancer Detection using Gene Data Pattern

Priyadharshini,

Zoraida

2019

IJEAT

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Cancer detecting technology plays a vital role in the medical community. Researches have shown that patients that are affected by cancer carry same type of genetic patterns in their DNA. With this in mind, this research work concentrates on analysing gene pattern for detecting cancer using deep learning algorithms. The Feedback based Adaptive Recurrent Neural Network (FA-RNN) approach is designed to classify and analyse the gene pattern recognition. The data augmentation is done to improve the quality of the input data from COSMIC dataset which includes the detection of missing values, removing the noise present in input using multiple imputations and reducing higher base value can be done using dimensionality reduction process. After obtaining the improved dataset, the training phase begins by estimating the exact weight value of feedback layer using feedback weight loop calculation technique to lessen number of repetition during training. Moreover, the error calculation is done to evaluate the exact weight values of feedback layer used for classification. Finally the classification is done by selecting the next appropriate hidden neuron using the neuron selection activation function. The performance of the Feedback based Adaptive Recurrent Neural Network technique can be analysed using the evaluation metrics accuracy, computation time and Root Mean Square Error (RMSE) and the attained results are compared with the Recursive Neural Network(RNN) and Convolutional Neural Network(CNN) algorithms. The obtained results such as higher accuracy, reduced RMSE and less computation time in Feedback based Adaptive Recurrent Neural Network indicates that it performs the enhanced operation than CNN and RNN.

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Occupancy prediction through Markov based feedback recurrent neural network (M-FRNN) algorithm with WiFi probe technology

Cited by 109 publications

References 56 publications

Development of a Consecutive Occupancy Estimation Framework for Improving the Energy Demand Prediction Performance of Building Energy Modeling Tools

Development of a Consecutive Occupancy Estimation Framework for Improving the Energy Demand Prediction Performance of Building Energy Modeling Tools

Predicting plug loads with occupant count data through a deep learning approach

Feedback Based Adaptive Recurrent Neural Network for Cancer Detection using Gene Data Pattern

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