Bi-level energy-efficient occupancy profile optimization integrated with demand-driven control strategy: University building energy saving

Jafarinejad, Tohid; Erfani, Arash; Fathi, Amirhossein; Shafii, Mohammad Behshad

doi:10.1016/j.scs.2019.101539

Cited by 42 publications

(20 citation statements)

References 71 publications

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“…The first category is computational intelligence at equipment level. Main control tools are different NNs 2‐29,110‐112 and Fuzzy control 30‐41,113 . Unlike conventional digital or computer control systems that can only perform one‐to‐one control output, Fuzzy tools can incorporate multiple variables into a single feedback sensor, thereby implementing a many‐to‐one control strategy.…”

Section: Aiif Developmentmentioning

confidence: 99%

“…After neural network (NN) or called artificial NN (ANN)-based training, these tools enable the input of multiple values with a single answer output, which is advanced to existing control systems. Represented research studies include ANN-based management of renewable energy sources 2 and optimal control of a heating system in a building 3 ; an ANN tool to predict daylight illuminance in office buildings 4 ; an ANN model development to predict hourly heating energy consumption of a model house 5 ; using ANN to predict performance of wet cooling tower 6 ; a bilevel timetable optimization by ANN 7 ; the comparison between model simulation and ANN for forecasting building energy consumption 8 ; an adaptive NN for chiller data analysis 9 ; NN tools for short-term energy-consumption prediction and power supply feedback control [10][11][12][13][14] ; a spiking NN (SNN) to construct intelligent control, hierarchical architecture, and a multiple agent system (MAS) 15 ; an SNN tool for energy-consumption analysis 16 ; an SNN for daily heating load prediction 17 ; a convolutional NN for short-term load forecasting 18 ; a recurrent NN for load prediction 19,20 ; an RNN for control optimization 21 ; a ward-type NN for air temperature prediction 22,23 ; and that for cooling tower approach temperature prediction 6 ; advanced NN with a modified learning algorithm 24,25 ; a wavelet-based NN for optimization of scheduling control 23,24 ; a supporting vector machine and regression (SVM&R) combined with NN for predicting heating and cooling loads 26,27 ; a simulatedbased NN for forecasting electrical energy consumption 28 ; NNs for demand side management 25 ; and feedforward NN-based indoor-climate control framework development for thermal comfort and energy saving in buildings. 29 Variou...…”

Section: Introductionmentioning

confidence: 99%

“…AI developments have provided a series of solutions for the whole building. However, a literature survey of 113 studies 2‐114 revealed that energy conservation control for the equipment, facility, and entire site/building accounted for 23.9%, 42.5%, and 33.6%, respectively. None of these studies have successfully integrated AI for saving three‐level building energy conservation, and this may be due to the lack of implementation frameworks.…”

Section: Introductionmentioning

confidence: 99%

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Artificial intelligence implementation framework development for building energy saving

et al. 2020

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In this study, artificial intelligence (AI) control tools were developed to construct an AI implementation framework for energy saving for buildings. Although numerous AI studies related to energy conservation have been conducted, most of them have reported computing algorithms and control effects for single objects. This is the first study to use a framework to integrate five-category AI control tools to execute three-level building energy conservation; the three levels consist of equipment-level control, facility-level control, and whole building energy saving. Energy-saving effects were tested in a real building. The complex three-floor building primarily with a total area of 9072 m 2 serves as an office space and a semiconductor production line. Seventy percent energy consumption comes from air conditioning system and motor power. Twenty percent is lighting system and the other 10% is plug power and office automation equipment. Before implementation, the yearly energy cost reached US$1004339. In 2018, an AI implementation framework was introduced to systematically deploy AI at the site. A total of 47.5%, 37%, and 36.9% of energy was saved at equipment, facility, and whole building levels; up to US$385203 was saved. These energy savings proved the feasibility of the implementation framework. Furthermore, unmet demands of AI studies were met, and an approach to fill the research gap is discussed. K E Y W O R D S artificial intelligence (AI), artificial intelligence implementation framework (AIif), building energy saving, equipment-level control, facility-level control List of Symbols, Abbreviations, and Notation: ω i , weighting coefficients; GS T , the sum of the global similarities between the selected m cases; MV i , the mean difference of the variable i; P j , proportion of the prediction; V 1 , variation of control parameter y; y ic , the neural outputs of variable i for the control; y ip , the neural outputs of variable i for past cases; AI

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Section: Aiif Developmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Artificial intelligence implementation framework development for building energy saving

et al. 2020

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“…Dey, et al [ 37 , 38 , 39 ] shows optimized energy saving and user convenience through machine learning based fault detection and diagnosis. Jafarinejad, et al [ 40 ] proposed a bi-level energy-efficient occupancy profile optimization method integrated with a demand-driven control strategy to optimize the energy consumption within in a university departmental building.…”

Section: Related Workmentioning

confidence: 99%

Reinforcement Learning-Based BEMS Architecture for Energy Usage Optimization

Park

Choi

et al. 2020

Sensors

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Currently, many intelligent building energy management systems (BEMSs) are emerging for saving energy in new and existing buildings and realizing a sustainable society worldwide. However, installing an intelligent BEMS in existing buildings does not realize an innovative and advanced society because it only involves simple equipment replacement (i.e., replacement of old equipment or LED (Light Emitting Diode) lamps) and energy savings based on a stand-alone system. Therefore, artificial intelligence (AI) is applied to a BEMS to implement intelligent energy optimization based on the latest ICT (Information and Communications Technologies) technology. AI can analyze energy usage data, predict future energy requirements, and establish an appropriate energy saving policy. In this paper, we present a dynamic heating, ventilation, and air conditioning (HVAC) scheduling method that collects, analyzes, and infers energy usage data to intelligently save energy in buildings based on reinforcement learning (RL). In this regard, a hotel is used as the testbed in this study. The proposed method collects, analyzes, and infers IoT data from a building to provide an energy saving policy to realize a futuristic HVAC (heating system) system based on RL. Through this process, a purpose-oriented energy saving methodology to achieve energy saving goals is proposed.

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“…Similar to other research in the fields of science and technology, AI techniques have widespread application in order to put forward reasonable evaluation in many engineering problems [9][10][11][12][13][14][15][16][17] of the energy consumption in buildings. In numerous types of artificial intelligence-based solutions, artificial neural network (ANN) is known as a recognized method that is largely employed for many prediction-based examples [18][19][20][21][22]. Similar studies are performed in regard to hybrid metaheuristic optimization approaches [23][24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Predicting Heating Load in Energy-Efficient Buildings Through Machine Learning Techniques

et al. 2019

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The heating load calculation is the first step of the iterative heating, ventilation, and air conditioning (HVAC) design procedure. In this study, we employed six machine learning techniques, namely multi-layer perceptron regressor (MLPr), lazy locally weighted learning (LLWL), alternating model tree (AMT), random forest (RF), ElasticNet (ENet), and radial basis function regression (RBFr) for the problem of designing energy-efficient buildings. After that, these approaches were used to specify a relationship among the parameters of input and output in terms of the energy performance of buildings. The calculated outcomes for datasets from each of the above-mentioned models were analyzed based on various known statistical indexes like root relative squared error (RRSE), root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (R2), and relative absolute error (RAE). It was found that between the discussed machine learning-based solutions of MLPr, LLWL, AMT, RF, ENet, and RBFr, the RF was nominated as the most appropriate predictive network. The RF network outcomes determined the R2, MAE, RMSE, RAE, and RRSE for the training dataset to be 0.9997, 0.19, 0.2399, 2.078, and 2.3795, respectively. The RF network outcomes determined the R2, MAE, RMSE, RAE, and RRSE for the testing dataset to be 0.9989, 0.3385, 0.4649, 3.6813, and 4.5995, respectively. These results show the superiority of the presented RF model in estimation of early heating load in energy-efficient buildings.

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Bi-level energy-efficient occupancy profile optimization integrated with demand-driven control strategy: University building energy saving

Cited by 42 publications

References 71 publications

Artificial intelligence implementation framework development for building energy saving

Artificial intelligence implementation framework development for building energy saving

Reinforcement Learning-Based BEMS Architecture for Energy Usage Optimization

Predicting Heating Load in Energy-Efficient Buildings Through Machine Learning Techniques

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