Research opportunities arising from control and optimization of smart buildings

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smart buildings Qianchuan Zhao CFINS, Dept.

1 ISA Workshop on Frontiers in Systems and Control Research opportunities arising from control and optimization of smart buildings Qianchuan Zhao CFINS, Dept. Automation and TNList, Tsinghua University

CFINS Yu-Chi Ho chair professor group and Center for Intelligent and Networked Systems (CFINS) were established in October 2001 to provide a physical and intellectual environment for the intelligent

2 CFINS Yu-Chi Ho chair professor group and Center for Intelligent and Networked Systems (CFINS) were established in October 2001 to provide a physical and intellectual environment for the intelligent analysis, design, and operation of complex and networked systems such as computer and communication networks, buildings, power systems, and supply chains by making innovative use of analytical methods and information technology. 2

3 What we mean by smart for buildings? 3

4 Potential of ICT technology MOORE s Law AI 4

5 What we mean by smart for buildings? Utilize information relevant to the whole building system thanks to IoT as a result of the fast drop in the cost of hardware for computing, storage and communication; Care about individual occupant thanks to the rapid development of machine learning techniques. 5

6 Energy consumption Type Building faction 40% (68%Electr. ) Transportation 40% Others 20% Energy saving for buildings has been omitted for long, it has great potential! 6

Energy consumption in buildings Office Building Elevator 3% Office Equipments 22% Other 10% Lights 28% HVAC 37% Hotel Elevator 9% Office Equipments

7 Energy consumption in buildings Office Building Elevator 3% Office Equipments 22% Other 10% Lights 28% HVAC 37% Hotel Elevator 9% Office Equipments 4% 18% Other Lights 25% HVAC 44% It was estimated that 20% ~ 30% energy saving can be achieved by optimizing the operation and control of buildings. 7

8 System Architecture Integrated building control for energy saving Data driven modeling + prediction Information fusion 8

9 Control and optimization of building energy system Energy supply in building Controllable devices Distribution Battery Elect. Lighting HVAC Solar Fuel cell Heat Wind CHP E-car Shading Window Micro-grid Minimization of energy cost Comfort: Temp. Humid. Illum. 9 CO2 Occupant demand

10 List of possible challenges Integrated control under full information may suffer the curse of dimensionality problem and time consuming evaluation of performance or constraints. Mache learning in general is a hard problem: design of a good ML algorithm also include many decision variables (model structure, parameters, implementation, input data ) 10

11 Ways to address the challenges According to NFLT, problem specific knowledge is needed to develop efficient solutions. Soft optimization for integrated control: OO, OCBA, COO, NP, ADP, EBO, IPA, Apply problem specific knowledge to reduce the search space for a good ML algorithm. 11

12 Illustration of COO G S N 12

13 Below we will use individual thermal comfort model as an example of ML in smart building applications. 13

14 S et point (oc ) E nergy consum ption (kw ) HVAC system Motivations First invented to serve the machine, manufacturing process, etc. --Set point oriented control When HVAC serves people. Set point oriented control like what they did on the machine S et point E nergy consum ption Set point Day 1 Day 2 Day 3 Day 4 Day /1/ /1/ /1/ /2/3 2008/2/8 2008/2/ /2/ /2/ /2/ /3/4 2008/3/9 2008/3/ /3/ /3/ /3/ /4/3 17 Tokyo Univ survey data FIT, Tsinghua Univ survey data 14

Motivations(cont d) Intelligent thermostat? (Perry D.

100 200 500 Completed Tasks Incomplete Tasks (minutes) 0.

User only inputs sensations Personalized and self-learning

15 Motivations(cont d) Intelligent thermostat? (Perry D. et al, 2011) Task 1: Set to Heat Time (s) Completed Tasks Incomplete Tasks (minutes) WEB TCH SMT BTN HYB User-oriented control system User only inputs sensations Personalized and self-learning Thermostats Human perception Control Perceive Indoor environment 15

Existing models The chamber study model Predicted Mean Vote-Predicted Percent Dissatisfied (PMV-PPD) model quantifies the thermal comfort concept as a mapping from the environmental factors and

16 Existing models The chamber study model Predicted Mean Vote-Predicted Percent Dissatisfied (PMV-PPD) model quantifies the thermal comfort concept as a mapping from the environmental factors and personal factors to a 7-level comfort value scale based on an average over a large data set. Environmental factors Personal factors air temperature radiant temperature relative humidity air velocity clothing level metabolic rate activity level PMV-PPD Model Thermal sensation A 7-level thermal sensation scale cold cool slightly cool neutral slightly warm warm PMV value hot 16

17 Existing models The models based on the human body physiology The two-node (core and skin) model The multi-segment mathematical model of human body The sensation and comfort model for human segments and the wholebody Field study comfort model The original models were presented by Humphreys and Nicol which described a strong relationship of the comfortable temperatures inside a building, to the mean temperatures prevailing inside the building Classified by de Dear and Brager as physiological, behavioral and psychological The ASHRAE adaptive model: ASHRAE standard SCATS: European adaptive comfort standard, EN

18 The main challenges Challenges All these works focus on average thermal comfort models instead of personalized comfort models. There exist less related literature and research on personalized comfort models. The cases for the group are more complicated and challenging. 18

19 Terminal Control Strategies for Energy and Comfort Controller Lights Blind Window AC Sensors Occupants Adaptive HMI metered 70.0 simulated Energy metering Room load W/m2 0.0 Energy Temp... Hot Cold Dry Humid Noisy Temp. Humidity Air speed CO 2 Acoustic level Illuminance Interpreter Dynamic Comfort Region CO 2 RH e 1 R H e o CO 2 Optimization Estimated comfort zone T T Human factors Psychology Engineering industrial design Building manager energy requirement Tsinghua-UTC Building Energy Energy, Safety and Control System Research Center. (CFINS, DBS, IE, CPSR) 19

The sensor box will record the environment measurements, store them

20 Sensation votes based model Voting software Sensors Setup: 1. Every one hour, the software will pop up to let the user vote. 2. The sensor box will record the environment measurements, store them in local computer through COM and further upload to the server database. 20

21 PDTC -- PMV framework Heat balance equation of human Mapping from the environment to the human thermal vote Cold Cool Neural Warm Hot Heat balance of human body M W C R E S 0 21

22 PDTC -- the proposed model Personalized Dynamic Thermal Comfort(PDTC) Perception thermal vote PTV m ( ) 0 m1 P m2t m3 R C a PDTC( k) m ( k) m ( k) P m ( k) t m ( k)( R C) 0 1 a 2 a 3 Considering the dynamics of human thermal perception a 22

Parameter estimation Parameter estimation Least squares N N k 1 2 { mˆ 0, mˆ 1, mˆ 2, mˆ 3} arg min ( k) ( PDTCk ( m0, m1, m2, m3) real vote) m0, m1, m2, m3 k 1 Recursive least squares estimation

23 Parameter estimation Parameter estimation Least squares N N k 1 2 { mˆ 0, mˆ 1, mˆ 2, mˆ 3} arg min ( k) ( PDTCk ( m0, m1, m2, m3) real vote) m0, m1, m2, m3 k 1 Recursive least squares estimation with forgetting factors Time-variant forgetting factors N N k 1 arg min ( k) ( k) m0, m1, m2, m3 k 1 ( k) [1, P, t, ( R C)] a PDTC( k) ( k) X ( k) a ( k) PDTC( k) truevote( k) 1, if the k and k-1 are in the same day ( k), otherwise 23

Results and validations Subject A Recursive Results 6 4 2 Parameter Values 0-2 12.1512.17 12.24 1.5 1.12 1.

24 Results and validations Subject A Recursive Results Parameter Values Subject B Recursive Results m 0 m 1 m 2 m 3 Office layout Time: From Nov, 2009 Jan, Month and Date 24

25 Results and validations Model validation accuracy Subject PDTC R-MSE PDTC R-Bias PDTC P-MSE PDTC P-Bias PMV P-MSE PMV P-Bias A B C D E F G H I R i R i Subject A Subject C R i R i Time offset Subject B Subject D , 1, 2, 3, 4 Bias and MSE Correlation coefficient of residuals and inputs 25

26 A study case of applications 6m Heat transfer of the interior walls Q iw Appliances heat emission Q eqp Lamps heat emission Q lt 6m Human body heat emission Min a, k a, k } Q occ { t, h all 0 1 ak, 2 ak, 3 Sensible and latent heating load for warming and humidifying outside air Q External Wall fa, S fa, L Q, Q East Outside ow, Q ow Heat transfer of the external wall and window s. t. m ( k) m ( k) P m ( k) t m ( k) ( R C) threshod Q h ( h, h ), t ( t a down up a down, t up ) k Personalized energy saving potentials Sensitivity: relative heating load decrease (%) A B C D E F G H I Higher energy cost, higher sentivity in comfor and energy saving tradeoff PMV sensitivity Increase of heating load relative to PMV based results (%) R Q t h Q t h Q t h * * * ( PDTC ( a, a) PMV ( a, a)) / PMV ( a, a) 100% S Q Q Q * * *, PDTC, threshold PDTC, threshold / PDTC PDTC, threshol d 100% 26

27 Limitations of the previous work in real application? Require the user to vote every one hour. Nonlinear comfort constraint when online implemented Can we be more user-friendly? 27

28 Complaint driven : more user-friendly Settings Users only complain whenever they felt necessary. Advantages Less demanding for users No interruption for users Close-loop control Human Machine Interface* *Y.Jiang, et al, A Human Machine Interface for Building Indoor Environment Control, Chinese Patent, ZL

29 Complaint driven : more user-friendly Challenges No intensity information in complaints : binary variables No comfort samples No-complaint periods have many possible explanations Few information of inner complaint region Environmental parameters are set around the comfort region boundary.(closedloop test-bed effects) 29

30 Problem formulation Problem formulation Only given the samples of target class*, i.e. a set of samples of a type of complaint χ = x 1, x 2,, x n,x i R 2, i.e. in the temperature and relative humidity plane, how to obtain a boundary description of the complaint region f(w, x) only based on the complaint samples χ. * Target class, the cold or hot complaints, which are from single subject. 30

31 Important properties Properties of the complaint region The complaint region in the environment parameter space (in normal environment parameter range) for a given complaint is connected. Additionally, some of the parameters are unidirectional 1. Existing researches conclude both the human comfort zone and discomfort zone are connected areas. 2. Unidirectional parameter in human perception generally exists. Some of the parameters are not clear. e.g. temperature in hot and cold complaints is unidirectional, relative humidity is not clear. 31

32 Multi-linear one-class classifier model Pareto-frontier set of the complaint samples A sample x i R 2 is in the pareto-frontier set with respect to the generalized inequality S iff there is no sample x j, j i such that x j S x i. where S is a proper cone* and x j S x i means x j x i S. Complaint samples Relative humidity The cone (direction) of 2 2 S { x R x(1 0 x ), (2) 0} Pareto frontier set in the 2 direction S No samples in this region Temperature * Stephen Boyd, Lieven Vandenberghe, Convex Optimization, Cambridge University Press,

33 Multi-linear one-class classifier model Multi-linear one-class classifier learning Least square linear estimation is performed for each of the pareto-frontier set V k and obtain a set of linear equations (classifiers). T 2 w k x c k ck 1 x V min ( ), 1,2 w k Pareto-frontier set plays the role of support vector in support vector description method Multi-linear approximation of the nonlinear boundary The complain region can be described by j T T 0, if wk xi ck 0, xi Vk fk ( x) w k x ck, k 1,2 0 k,otherwise 33

34 Multi-linear one-class classifier model Performance metrics False Negative Rate (Missing detection rate): the rate of complaints that were missed. False Positive Rate (False detection rate): the rate of complaints that were mistaken as comfort. Empirical Rule If the subject has not complained for 20 minutes and he/she will not complain for next 20minutes, the current environment conditions are regarded as comfort samples. FPR 1 comfort C { yi C } Ncomfo rt i 1 N I 1. The empirical rule is based on the results of transient thermal comfort research. 2. The higher FPR, the more conservative of the classifier is. 34

Human Machine Interface Sensors Radiant ceiling *Zhuo Mao, Fulin Wang, Teng Gao, Yunchuang Dai, Qianchuan Zhao, Yin Zhao, Biao

35 Experiment test-bed* Experiment settings Touch screen Human Machine Interface Dedicated HVAC and other terminals Integrated sensors and computers Closed-loop operation mode in test-bed. Human Machine Interface Sensors Radiant ceiling *Zhuo Mao, Fulin Wang, Teng Gao, Yunchuang Dai, Qianchuan Zhao, Yin Zhao, Biao Sun, Jing Guo, and Fan Zhang, Research of the room occupant complaining behavior pattern for the indoor environmental control, Advanced Materials Research Vols (2012) pp

36 Relative humidity % Relative humidity % Relative humidity % Relative humidity % Results of the experiment data 60 Subject A FPR = Subject B FPR = Temperature 0 C Temperature 0 C Subject C 65 FPR =0.77 FPR = Subject D Temperature 0 C Temperature 0 C Green polygon presents the parameter region of the experiment FPR is estimated as FPR C 36

37 Relative humidity % Relative humidity % Results of the experiment data 65 Subject B 65 Subject D FPR =0.24 FPR = Temperature 0 C Temperature 0 C 1. Cold complaints usually occur in the lower temperature part and while hot complaints in the higher part. 2. Data are collected in 3-4 continuous days during their experiments. 3. Ambiguous region, which both hot and cold complaint had occurred exists. 37

38 Results of the experiment data Comparison with the PMV model Hot Complaints 75 Relative humidity (%) Cold Complaint Region 3. Unexplored Region 2. Possible Uncomfortable region 1. Possible Comfort Region Cold Complaints Hot Complaint Region Relative humidity % Temperature ( o C) Temperature 0 C PMV numerical results in temperature and relative humidity plane. The clothing index was chosen as 0.6 and air velocity was 0, which is accordance with our experiment conditions 1. Complaint-based comfort model may have a larger complaint area than the PMV model, which indicates that indoor environment control based on PMV may cause complaints. 2. Different regions in the learning results represent different perceptions. 38

39 Performance analysis Comparison with other models False Negative Rate Comparison Subjects Fisher Linear discriminant model SVM model Proposed model Hot Cold Hot Cold Hot Cold A B C D E F Leave-one-out methods were utilized to evaluate the FNR for each methods. 2. Comfort samples were extracted from the experiment record according to the empirical rule in previous slide. 3. SVM model using the linear kernel function.* The proposed model has low false negative rate. *Richard O.Duda, Peter E.Hart and David G.Stork, Pattern Classification,2nd edition, John Wiley & Sons, Inc,

40 Experimental validation 40

41 Experimental valuation * * 41

Defining group comfort region as the intersection of all group member s individual

42 Group thermal comfort model The group comfort zone model We introduce here is a quite natural one: take the convex hull of the individual comfort zones of the group. Defining group comfort region as the intersection of all group member s individual comfort regions or the intersection of those of the majority when there are conflicts. 42

Experiment results The comparison with PMV Large group in Lanzhou Testbed It is obvious that the individual differences in thermal preference often incur dissatisfactions in the

43 Experiment results The comparison with PMV Large group in Lanzhou Testbed It is obvious that the individual differences in thermal preference often incur dissatisfactions in the group. This indicates that the average model, such as PMV, may have bias in predicting the thermal comfort for large group. Pareto frontier set(cold) Pareto frontier set (hot) 43

44 Challenges Summary Accurate occupant counting or localization problem See: T. Labeodan, W. Zeiler, G. Boxem, et al. Occupancy measurement in commercial office buildings for demand-driven control applications: A survey and detection system evaluation. Energy and Buildings, 2015, 93: Data Mining for integrated building control and optimization See: F. Xiao, C. Fan. Data mining in building automation systems for improving building operational performance. Energy and Buildings, 2014, 75: F. Cheng, X. Fu, C. Yan. A framework for knowledge discovery in massive building automation data and its application in building diagnostics. Automation in Construction, 2015, 50:

45 Links IEEE RAS TC on Smart Buildings Q. Jia, Q. Zhao, H. Darabi, et al. Smart building technology. IEEE Robotics & Automation Magazine, 2014, 21(2): IFAC TC on Smart Cities: Q. Zhao, Research opportunities arising from control and optimization of smart buildings, Control Theory and Technology, Vol. 15, No. 1, pp , February

46 References Jiang Y, Wang FL, Jiang ZY, Hou Y, Zhao QC, Liu Y, Zhang F, Jiang Y, Human- Computer Interface of Two-Way Interactive Architectural Environment Control System, International Patent, WO/2012/019328, Application No. PCT/CN2010/ Zhao QC, Zhao Y, Wang FL, Wang JL, Jiang Y, Zhang F, A data-driven method to describe the personalized dynamic thermal comfort in ordinary office environment: from model to application, Building and Environment, 72( ), 2014 Zhao QC, Zhao Y, Wang FL, Jiang Y, Jiang Y, Zhang F, Preliminary study of learning individual thermal complaint behavior using one-class classifier for indoor environment control, Building and Environment, 72( ), 2014 Zhao QC, Chen ZJ, Wang FL, Jiang Y, Ding JL, Experimental study of group thermal comfort model, 2014 IEEE International Conference on Automation Science and Engineering (CASE), pp: Z. Cheng, Q. Zhao, F. Wang, Y. Jiang, L. Xia, and J. Ding, Satisfaction based Q- learning for integrated lighting and blind control, Energy and Buildings, vol. 127, pp , F. Wang, Z. Chen, Q. Feng, Q. Zhao, Z. Cheng, Z. Guo, Z. Zhong, Experimental comparison between set-point based and satisfaction based indoor thermal environment control, Energy and Buildings, vol. 128, pp ,

47 Thanks Prof. Ho for your inspiring guidance over the years 47

48 Multi-linear one-class classifier model Determine pareto-frontier sets* of samples m A sample x is in the pareto-frontier set with respect to generalized i R inequality iff there does not exist any other sample, x S j, i jsuch that x x. where S is a proper cone in R m j S i Generalized inequality x j S x i means x j x i S Example: 2 2 S x R x(1) x(2) 0, 0 By incorporating the prior knowledge, the pareto-frontier set of a certain class of samples represent boundary profiles in specified direction which we interested most. * Stephen Boyd, Lieven Vandenberghe, Convex Optimization, Cambridge University Press, /43

49 Unbiased theoretically? Expression noise when survey or vote Percentage of Rankings Received by Any Term Across Subjects WW Ranks IMPOSSIBLE IMPROBABLE UNLIKELY POSSIBLE LIKELY PROBABLE CERTAIN 100 WN Ranks IMPOSSIBLE % IMPROBABLE % UNLIKELY % POSSIBLE % PROBABLE LIKELY % % % CERTAIN Jaffe-katz and Budescu, /43

50 An intuitive illustration Cold Cool Neural Warm Hot True vote Noise distribution Noise distribution 50/43

51 Problems in the identification framework Output-dependent observation noise Inputs u Unknown Systems y Observation Noise ŷ Identification ˆ Observation noise is dependent on the system output 51/43

52 Problem in the identification framework Unbiased estimation of the system parameters? Inconsistency of the noise at different outputs Output-dependent mean value (cannot remove the noise by average) Inputs u Unknown Systems y Observation Noise ŷ Identification ˆ E ˆ 52/43

53 Proposed identification methods Key ideas: First identify the noiseless output y(u i ) using the noise model. Decouple the relationship between the parameters and the noise. Then identify the system parameters θ. Return to the normal system identification. Inputs u Unknown Systems y Observation Noise ŷ y(u i ): estimation of y u i. θ: estimation of θ. Identification ˆ E ˆ ( ) yu i Estimate the noiseless output 53/43

54 Noise probability densitity Noise disribution densitity Noise model Output-dependent bounded noise model The noise is bounded and its bound is related to the noiseless output. The probability density function has peak value at 0. Truncated distributions -- examples for different outputs in a bounded range 0.7 Truncated Normal Distribution (TN) Truncated Double Exponential Distribution (TDE) TN(a=-3,b=3, = 1, y = -2.5) TN(a=-3,b=3, = 1, y = -1.5) TN(a=-3,b=3, = 1, y = 0) With noise parameter: σ TDE(a=-3,b=3, = 1,y = -2.5) TDE(a=-3,b=3, = 1,y = -1.5) TDE(a=-3,b=3, = 1,y = 0) With noise parameter: λ w w 54/43

55 Proposed identification methods When the noise parameter (δ) is known Choose the input as uk* I i, i 1,2,..., I d, k 0,1,2,..., K u u... u u 0* I i 1* I i k* I i i Construct the following identification equation K 1 y( u ) E( w( y( u ), )) yˆ ( u ), i 1,2,..., I i i k* I i K 1 k 0 This is the function of y( u ) when the noise parameter is known. i An explicit for of the equation, for example, TN model, is a y( ui) b y( ui) ( ) ( ) K 1 y( u ) ˆ i y( uk* I i ), i 1,..., I b y( ui) a y( ui) 1 k 0 ( ) ( ) K Where φ, Φ are the p.d.f. and c.d.f. of standard normal distribution 55/43

56 Proposed identification methods When the noise parameter (δ) is known (cont d) If the identification equation has unique solution K 1 y( u ) E( w( y( u ), )) yˆ ( u ), i 1,2,..., I i i k* I i K 1 k 0 yu ( i ) K The identification can be done by solving the following noiseless identification T Where [ ( u ), ( u ),..., ( u )] T and Y [ y( u ), y( u ),..., y( u ) ] T. Y K i 2 I K 1 K 2 K I K Note: 1. The solution of identification is related to the number of repeated input K. 2. The inputs ui, i 1,2,... I should satisfy the Persistent Exciting Condition. 3. We name the identification method as Basic Identification Algorithm (BIA). 56/43

57 Proposed identification methods When the noise parameter (δ) is unknown Underdetermined problem: I identification equations with I + 1 unknown variables y( u ), i 1,2,..., I,. Introduce an additional criterion i Maximum likelihood under the constraint of identification equations s.t. max log L(, D) K 1 y( u ) E( w( y( u, ))) yˆ ( u ), i 1,..., I i i k* I i K 1 k 0 T y( u ) ( u ), i 1,..., I i i Note: 1. When the system is identifiable, then given δ, there is unique θ and y u i. 2. The unknown parameter is usually a scalar and the optimization is converted to the one-dimension search problem, where each search step involves a procedure of identification when the noise parameter is known. 3. We name the algorithm as Joint Identification Algorithm (JIA). 57/43

58 Algorithms Theorem 1 Under the condition that the identification equation has unique solution, then the proposed algorithm can obtain the unbiased estimate of the unknown system parameter when K 58/43

59 Algorithms(cont d) Theorem 2 When the identification equations have unique solution for different, the results of Joint Identification converge to the true system parameter θ and noise parameter δ with in probability when K. 59/43

60 Numerical test and application 60/43

61 Numerical test and application Application in PDTC model Y. Zhao and Q. Zhao, System Identification for Output-dependent Bounded Noises and its Application in Learning Personalized Thermal Comfort Model, To appear in IEEE Proceedings of International Conference on Robotics and Automation, Karlsruhe, Germany, /43

CAE 331/513 Building Science Fall 2017

CAE 331/513 Building Science Fall 2017 September 19, 2017 Human thermal comfort Advancing energy, environmental, and sustainability research within the built environment www.built-envi.com Twitter: @built_envi