Data analytics and optimization of an ice-based energy storage system for commercial buildings

Luo, Na; Hong, Tianzhen; Li, Hui; Jia, Ruoxi; Weng, Wenguo

doi:10.1016/j.apenergy.2017.07.048

Cited by 77 publications

(14 citation statements)

References 32 publications

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“…XGBoost and LSTM were implemented with the XGBoost library [31] and Keras [32] in Python. These two tools are powerful, as most Kaggle competition 3 winners used either the XGBoost library (for shallow machine learning) or Keras (for deep learning) [33]. The first objective of this study was to compare the performance of shallow and deep learning under the context of building load prediction.…”

Section: Research Gap and Objectivesmentioning

confidence: 99%

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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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Section: Research Gap and Objectivesmentioning

confidence: 99%

“…To reduce building energy usage and its associated carbon emissions, building thermal load prediction could play an important role. It has wide applications in HVAC control optimization [2], thermal energy storage operation [3], energy distribution system planning [4], and smart grid management [5] among others.…”

Section: Introductionmentioning

confidence: 99%

Building thermal load prediction through shallow machine learning and deep learning

2020

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“…Table 5 summarizes the major machine learning algorithms used in building control stage. MPC [96], [97], [98], [99] Kalman Filter [100], [101] Generic Algorithm [102] RL: value based [103], [104], [105], [106], [107], [108] RL: actor critic [109], [110] Learning building thermal dynamics for building control RC model and regression [97], [111], [112], [113] RC model and Generic Algorithm [114] Lighting control RL: value based [115] Window control RL: value based [116] Thermal Energy Storage control Non-linear programming [117] RL: value based [118] RL: actor critic [119], [120] Hot water control RL: value based [121] RL: actor critic [122] Comfort improvement…”

Section: Machine Learning For Building Controlmentioning

confidence: 99%

“…In addition to thermal comfort, visual comfort [115] and indoor air quality [116] have been considered in previous studies. Other similar goals complementary to energy conservation include minimizing carbon emission, minimizing operation costs [117], and enhancing grid interactivity (such as load shifting, or maximizing the self-consumption of the local PV production [121]). Those goals could be encoded easily by multiplying the energy consumption with a time-of-use weight, which might be a time-of-use utility rate for the cost optimization problem or a time-of-use carbon emission rate for carbon emission minimization.…”

Section: Problem Formulationmentioning

confidence: 99%

State-of-the-art on research and applications of machine learning in the building life cycle

Hong

Wang

Luo

et al. 2020

Energy and Buildings

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Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science.

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“…To achieve an energy efficient heat storage and release strategy, it is required to predict the building load first. In other words, building load prediction is the input and prerequisite of predictive control, and the key to improve the performance of predictive building control and to save energy costs [7].…”

Section: Importance Of Internal Heat Gains Predictionmentioning

confidence: 99%

Data fusion in predicting internal heat gains for office buildings through a deep learning approach

2019

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Heating, Ventilation, and Air Conditioning (HVAC) is a major energy consumer in buildings. The predictive control has demonstrated a potential to reduce HVAC energy use. To facilitate predictive HVAC control, internal heat gains prediction is required. In this study, we applied Long Short-Term Memory Networks, a special form of deep neural network, to predict miscellaneous electric loads, lighting loads, occupant counts and internal heat gains in two United States office buildings. Compared with the predetermined schedules used in American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) standard 90.1, the Long Short-Term Memory Networks method could reduce the prediction errors of internal heat gains from 12% to 8% in Building A, and from 26% to 16% in Building B. It was also found that for internal heat gains prediction, miscellaneous electric loads is a more important feature than occupant counts for two reasons. First, miscellaneous electric loads is the best proxy variable for internal heat gains, as it is the major component of and has the highest correlation coefficient with the internal heat gains. Second, miscellaneous electric loads contain valuable information to predict occupant count, while occupant count could not help improve miscellaneous electric loads prediction. These findings could help researchers and practitioners select the most relevant features to more accurately predict internal heat gains for the implementation of predictive HVAC control in buildings.

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Data analytics and optimization of an ice-based energy storage system for commercial buildings

Cited by 77 publications

References 32 publications

Building thermal load prediction through shallow machine learning and deep learning

Building thermal load prediction through shallow machine learning and deep learning

State-of-the-art on research and applications of machine learning in the building life cycle

Data fusion in predicting internal heat gains for office buildings through a deep learning approach

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