Prediction of occupancy level and energy consumption in office building using blind system identification and neural networks

Wei, Yixuan; Liang, Xin; Pan, Song; Wu, Jinshun; Zhang, Xingxing; Han, Mengjie; Wei-ya, Zhang; Xie, Jingchao; Li, Qingping

doi:10.1016/j.apenergy.2019.02.056

Cited by 117 publications

(36 citation statements)

References 82 publications

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“…Chen et al (2019) [16] proposed a novel ensemble ELM algorithm for human activity recognition using smartphone sensors. For the prediction of occupancy level and energy consumption in an office building, Wei et al (2019) [17] applied blind system identification and neural networks. Jiang et al (2020) [18] proposed a model based on a novel ensemble extreme learning machine technique that can estimate the occupancy level from carbon dioxide concentration.…”

Section: Introductionmentioning

confidence: 99%

Occupancy Prediction Using Differential Evolution Online Sequential Extreme Learning Machine Model

et al. 2020

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Despite increasing energy efficiency requirements, the full potential of energy efficiency is still unlocked; many buildings in the EU tend to consume more energy than predicted. Gathering data and developing models to predict occupants’ behaviour is seen as the next frontier in sustainable design. Measurements in the analysed open-space office showed accordingly 3.5 and 2.7 times lower occupancy compared to the ones given by DesignBuilder’s and EN 16798-1. This proves that proposed occupancy patterns are only suitable for typical open-space offices. The results of the previous studies and proposed occupancy prediction models have limited applications and limited accuracies. In this paper, the hybrid differential evolution online sequential extreme learning machine (DE-OSELM) model was applied for building occupants’ presence prediction in open-space office. The model was not previously applied in this area of research. It was found that prediction using experimentally gained indoor and outdoor parameters for the whole analysed period resulted in a correlation coefficient R2 = 0.72. The best correlation was found with indoor CO2 concentration—R2 = 0.71 for the analysed period. It was concluded that a 4 week measurement period was sufficient for the prediction of the building’s occupancy and that DE-OSELM is a fast and reliable model suitable for this purpose.

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Section: Introductionmentioning

confidence: 99%

Occupancy Prediction Using Differential Evolution Online Sequential Extreme Learning Machine Model

et al. 2020

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“…In subsequent years more complicated methods have been used. Some popular choices include support vector machine (SVM) [19]; artificial neural networks (ANN) [20]; extreme learning machine [21]; regression tree [22]; random forest [23]; and Hierarchical Mixture of Experts [24].…”

Section: Previous Workmentioning

confidence: 99%

Building thermal load prediction through shallow machine learning and deep learning

2020

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Building thermal load prediction informs the optimization of cooling plant and thermal energy storage. Physics-based prediction models of building thermal load are constrained by the model and input complexity. In this study, we developed 12 data-driven models (7 shallow learning, 2 deep learning, and 3 heuristic methods) to predict building thermal load and compared shallow machine learning and deep learning. The 12 prediction models were compared with the measured cooling demand. It was found XGBoost (Extreme Gradient Boost) and LSTM (Long Short Term Memory) provided the most accurate load prediction in the shallow and deep learning category, and both outperformed the best baseline model, which uses the previous day's data for prediction. Then, we discussed how the prediction horizon and input uncertainty would influence the load prediction accuracy. Major conclusions are twofold: first, LSTM performs well in shortterm prediction (1h ahead) but not in long term prediction (24h ahead), because the sequential information becomes less relevant and accordingly not so useful when the prediction horizon is long. Second, the presence of weather forecast uncertainty deteriorates XGBoost's accuracy and favors LSTM, because the sequential information makes the model more robust to input uncertainty. Training the model with the uncertain rather than accurate weather data could enhance the model's robustness. Our findings have two implications for practice. First, LSTM is recommended for short-term load prediction given that weather forecast uncertainty is unavoidable. Second, XGBoost is recommended for long term prediction, and the model should be trained with the presence of input uncertainty.

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“…For example, Guo et al used machine learning-based models to predict energy demands for building heating [42]. Wei et al applied blind system identification and neural networks to predict office building energy use along with occupancy [43]. Fan et al assessed deep recurrent neural network-based strategies for short-term building energy predictions [44].…”

Section: Review Of Data-driven Studies For Ubemmentioning

confidence: 99%

Forecasting district-scale energy dynamics through integrating building network and long short-term memory learning algorithm

Wang

Hong

et al. 2019

Applied Energy

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With the development of data-driven techniques, building energy prediction on a district level has attracted increasing attention in recent years for revealing energy use patterns and reduction potentials. However, large-group building data acquisition is more difficult as buildings interact with each other at the city level.To reduce data cost and consider the inter-building impact on the data-driven building energy model, this study proposes a deep learning predictive approach to integrate building networks with a long short-term memory model to create building energy model on a district scale. The building network was calculated via correlations between the energy use intensity of buildings; buildings with high correlation were applied in predictive model to reduce the required number of buildings. To validate the proposed model, five typical building groups with energy use from 2015 to 2018 on two institutional campuses were selected to develop and test the proposed model with the TensorFlow tool. Based on error assessments on the predicted building group energy use, the results suggest that for total building energy use intensity prediction, the proposed model can achieve a mean absolute percentage error of 6.66% and a root mean square error of 0.36 kWh/m 2 , compared to 12.05% and 0.63 kWh/m 2 for artificial neural network model and to 11.06% and 0.89 kWh/m 2 for support vector regression model. Therefore, the proposed model integrating building network and

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Prediction of occupancy level and energy consumption in office building using blind system identification and neural networks

Cited by 117 publications

References 82 publications

Occupancy Prediction Using Differential Evolution Online Sequential Extreme Learning Machine Model

Occupancy Prediction Using Differential Evolution Online Sequential Extreme Learning Machine Model

Building thermal load prediction through shallow machine learning and deep learning

Forecasting district-scale energy dynamics through integrating building network and long short-term memory learning algorithm

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