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Artificial Intelligence-Assisted Heating Ventilation and Air Conditioning Control and the Unmet Demand for Sensors: Part 1. Problem Formulation and the Hypothesis

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In this study, information pertaining to the development of artificial intelligence (AI) technology for improving the performance of heating, ventilation, and air conditioning (HVAC) systems was collected. Among the 18 AI tools developed for HVAC control during the past 20 years, only three functions, including weather forecasting, optimization, and predictive controls, have become mainstream. Based on the presented data, the energy savings of HVAC systems that have AI functionality is less than those equipped with traditional energy management system (EMS) controlling techniques. This is because the existing sensors cannot meet the required demand for AI functionality. The errors of most of the existing sensors are less than 5%. However, most of the prediction errors of AI tools are larger than 7%, except for the weather forecast. The normalized Harris index (NHI) is able to evaluate the energy saving percentages and the maximum saving rations of different kinds of HVAC controls. Based on the NHI, the estimated average energy savings percentage and the maximum saving rations of AI-assisted HVAC control are 14.4% and 44.04%, respectively. Data regarding the hypothesis of AI forecasting or prediction tools having less accuracy forms Part 1 of this series of research.
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sensors
Article
Artificial Intelligence-Assisted Heating Ventilation
and Air Conditioning Control and the Unmet
Demand for Sensors: Part 1. Problem Formulation
and the Hypothesis
Chin-Chi Cheng and Dasheng Lee *
Department of Energy and Refrigerating Air-Conditioning Engineering, National Taipei University of
Technology, Taipei 10608, Taiwan; newmanch@ntut.edu.tw
*Correspondence: f11167@ntut.edu.tw; Tel.: +886-2-27712171 (ext. 3510)
Received: 12 February 2019; Accepted: 28 February 2019; Published: 6 March 2019


Abstract:
In this study, information pertaining to the development of artificial intelligence (AI)
technology for improving the performance of heating, ventilation, and air conditioning (HVAC)
systems was collected. Among the 18 AI tools developed for HVAC control during the past
20 years, only three functions, including weather forecasting, optimization, and predictive controls,
have become mainstream. Based on the presented data, the energy savings of HVAC systems that
have AI functionality is less than those equipped with traditional energy management system (EMS)
controlling techniques. This is because the existing sensors cannot meet the required demand for
AI functionality. The errors of most of the existing sensors are less than 5%. However, most of the
prediction errors of AI tools are larger than 7%, except for the weather forecast. The normalized
Harris index (NHI) is able to evaluate the energy saving percentages and the maximum saving
rations of different kinds of HVAC controls. Based on the NHI, the estimated average energy savings
percentage and the maximum saving rations of AI-assisted HVAC control are 14.4% and 44.04%,
respectively. Data regarding the hypothesis of AI forecasting or prediction tools having less accuracy
forms Part 1 of this series of research.
Keywords:
artificial intelligent (AI); heating ventilation and air conditioning (HVAC) system;
forecasting/predicting error; priori information notice (PIN); energy management system (EMS);
energy savings; normalized Harris index (NHI)
1. Introduction
Heating, ventilation, and air conditioning (HVAC) systems provide a suitable living environment
with thermal comfort and air quality. These mechanic–electrical systems include several types, such as
air conditioners, heat pumps, furnaces, boilers, chillers, and packaged systems [
1
]. In most of the
countries, the building sector accounts for nearly 40% of the total consumed energy [
2
]. For every
building type, HVAC and lighting systems occupy more than half of the energy consumption [
3
].
A large fraction of the increasing energy expenditure for the buildings was because of the extending
HVAC installations for better thermal comfort and air quality [
4
]. Therefore, the HVAC system plays
an important role in the energy efficiency of buildings. Improving the control of HVAC operations and
the efficiency of the HVAC system can save significant energy, increase thermal comfort, and contribute
to improved indoor environmental quality (IEQ) [
5
]. Artificial intelligence (AI) was founded as an
academic discipline in 1956. In contrast to human intelligence, AI demonstrates machine intelligence
and imitates human behaviors through mathematical coding and mechanical works. In 1997, an AI
program known as Deep Blue defeated the reigning world chess champion, Garry Kasparov [
6
].
Sensors 2019,19, 1131; doi:10.3390/s19051131 www.mdpi.com/journal/sensors
Sensors 2019,19, 1131 2 of 30
It was the first time that the chess-playing computer performed better than a human. That moment
was a turning point in the development of AI that enabled AI to be utilized more in a wider range
of applications.
In this study, how AI could improve the performance of heating, ventilation, and air conditioning
(HVAC) systems was investigated. A total of 783 articles, which were related to AI research and its
application on HVAC systems, was collected from three databases, including the Science Direct on
Line (SDOL), IEEE Xplore (IEL Online), and MDPI. The MDPI database is a publisher of open access
journals. Following the preferred reporting items for systematic reviews and meta-analyses (PRISMA)
method [
7
] for reporting, systematic review, and meta-analysis, the collected articles were screened,
and only 97 full-text articles met the requirements. All of the selected articles regard theoretical work
and practical experiments about HVAC control. Detailed information of these articles, including the
study cases, AI tools, or developments, and the improved performance of HVAC systems, are presented
in Section 2and summarized in Table 1. Among the 18 developed AI tools, only two methodologies
have become mainstream elements of HVAC controls over the past 20 years, which are the forecasting
and optimization and the predictive controls. These two main methodologies will be discussed
in Section 3.
Even though the development of AI tools for HVAC systems is more than two decades old,
the performance of HVAC systems controlled by AI tools has been unsatisfactory overall. Their energy
savings, energy consumption, precision of heating and cooling based on load forecasting, and the
predictive ability of the predictive controls, will be discussed in Section 4. Based on [
8
], from 1976
to 2014, the average energy savings of HVAC systems by applying the scheduling control technique
reached 14.07%. The maximum energy savings of HVAC systems was 46.9% after applying smart
sensors for smart air conditioners in 2014 [
9
]. However, from 1997 to 2018, the average energy savings
of HVAC systems using AI tools reached 14.02%. The maximum energy savings when applying
case-based reasoning (CBR) controlling tools for the HVAC systems in an office building was only 41%
in 2014. Therefore, the energy savings of HVAC systems after applying AI tools was less than that of
traditional energy management system (EMS) controlling techniques.
This study will be conducted in three parts, including (1) problem formulation and the hypothesis,
(2) simulations and verification, and (3) confirmatory experiments. The first part, problem formulation
and the hypothesis, will analyze the problem of HVAC systems using AI tools having less accuracy
of forecasting, or and a prediction of the tools that result in poor energy savings is hypothesized.
If forecast accuracies could be improved and prediction errors could be reduced, the energy savings
of HVAC systems would improve. From the 35 collected articles with information regarding sensor
specifications, the literature states that the existing sensors are for feedback control, not prediction,
and therefore lack the capability to provide priori information notice (PIN). Hence, an innovative PIN
sensor design and more precise predictive control is presented in this study as the solution to increase
the energy savings of HVAC systems.
The second part of the study covers the simulation and verification of the energy-saving
hypothesis and PIN sensor design through numerical simulation. Through numerical simulation,
the calculated energy savings of an HVAC system using a PIN sensor will be provided. The third
part consists of the confirmatory experiment where the designed PIN sensors are utilized under the
various operating conditions of an HVAC system in an environmentally controlled room to measure
energy consumption. The energy consumption of the HVAC system utilizing the PIN sensors and AI
tools will be compared with those employing the proportional–integral–differential (PID) controllers,
and the simulation results are analyzed to give evidence of the hypothesis presented in this study.
Sensors 2019,19, 1131 3 of 30
Table 1. Artificial intelligence (AI) developments for heating, ventilation, and air conditioning (HVAC) systems and the obtained key results.
Year HVAC System AI Development Key Results Ref.
1997
Case #1. A medium-sized utility from the
Midwestern United States (US); Case #2. A large
utility from the Midwestern US
Operation decision environment (ODE) architecture Model-based control and fault diagnosis [10]
1997 HVAC system for occupant comfort and efficient
running costs Knowledge-based system (KBS) for predictive control Based on pre-programmed load priorities, 20% electricity savings was
achieved [11]
1998 MACQU software applied to a greenhouse Native fuzzy KBS at the supervisory level Control loop optimization and 12% energy savings [12]
1998 Expert system in commercial buildings KBS for energy conservation programs Cost savings up to 60% [13]
2000 HVAC system with variable air volume (VAV)
coils and constant air volume (CAV) coils
Genetic algorithm (GA), cost estimation and
model-based predictor
Simulation results show that the overall energy savings were 0.1%, 0.2%,
1.8% and 1.9% less than the original status [14]
2001 Prediction of heating and cooling loads at
residential buildings Static neuro network (SNN) development for prediction Load curve fitting with an R-square value up to 0.9887
Prediction error ranges from 2.5% to 8.7% [15]
2001
Use of artificial neuro networks (ANNs) in solar
radiation and wind speed prediction, photovoltaic
systems, building services, and load forecasting
and prediction
ANNs for modeling a solar steam generator, modeling of
solar domestic water heating systems, and forecasting the
building thermal loads
R-square value of load fitting ranges from 0.9733 to 0.9940.
The prediction errors are within 1.9–5.5%. [16]
2001 Optimal heating control of a passive solar
commercial building
Smart heating controller with the cost function can combine
comfort level and energy consumption
Energy savings of maintaining or improving a thermal comfort
are about 9% [17]
2002 House_n demonstration at Massachusetts
Institute of Technology
Saving energy, maintaining air quality and thermal comfort
using data analysis Energy savings are about 14% [18]
2002 SNN for analyzing energy consumption in
residential buildings Model-based control for energy savings Energy savings range from 5% to 15% [19]
2003 Building automation and energy
management using AI
Distributed AI development for demand-side management
(DSM) and scheduling energy consumption according to
energy tariff
DSM-abled devices can save up to 40% on energy costs
based on 24-h analysis [20]
2003 Fuzzy controller for the management of an
indoor environment
Five fuzzy controllers include fuzzy P, fuzzy
proportional–integral–differential (PID), fuzzy PI, fuzzy PD
and adaptive fuzzy PD
While maintaining predicted mean vote (PMV) within 0–0.1 and indoor
CO2ppm increased less than 20 ppm, fuzzy P controller had the best
performance, heating and cooling energy can be reduced up to 20.1%.
[21]
2003 ANNs in the optimal operation of
HVAC equipment
ANN was developed for predicting the optimal start times
of a heating system in a building
In 27 instances, a clear linear relationship between prediction and real
data was shown by the R-square values ranging from 0.968 to 0.996. [22]
2004 ANNs for load forecasting of Taiwan
power system
An integrated, evolving fuzzy neuro network and simulated
annealing (AIFNN) developed for load forecasting
Compared with traditional ANNs, AIFNN can reduce prediction errors
up to 3% [23]
Sensors 2019,19, 1131 4 of 30
Table 1. Cont.
Year HVAC System AI Development Key Results Ref.
2005 On-line building energy consumption prediction
through adaptive ANN
Adaptive ANN model fits the unexpected pattern changes
of the incoming data of chillers at a Laval building operated
from 7:30 to 23:00, Monday to Friday
The prediction accuracy is measured by the coefficient of variation (CV)
and the root mean square error (RMSE). For the Laval building case, the
CV is 0.20 and the RMSE = 27.0 kW. With respect to the total power
consumption ~180 kw, the prediction error is 15%.
[24]
2005 Energy forecast of intelligent buildings located at
US and United Kingdom (UK)
Increased return on investment (ROI) by using fuzzy
multi-criteria decision-making method (DMM) 3% cost savings can be achieved with AI-assisted decision making. [25]
2005 Adaptive control of home environment
(ACHE) at Colorado Distributed AI development and integrated with sensors Sensors of electrical consumption with ANN adapt to the
habits of inhabitants [26]
2005 Predicting hourly energy
consumption in buildings
ANN development for predicting short-term energy
consumption and feedback control Feedback ANN for highly efficient energy supply [27]
2005 Prediction of building energy consumption
in tropical regions
Support vector machine (SVM) development for accurate
prediction based on weather forecast data
Summertime energy consumption can be accurately predicted within an
error rate of less than 4.5% [28]
2005 Prediction of daily heating loads of UK buildings
SNN development for daily heating load predictions based
on one year of sensor data Prediction error rate of less than 3.0% [29]
2006
Electric load forecasting through the use of
data from the East-Slovakia Power
Distribution Company
SVM model development for the forecasting of a test set in
January 1999 Mean average percent error (MAPE) rate of 1.93% [30]
2006 Centralized HVAC system Multi-agent structure development for thermal
comfort control
Control accuracy of around 89% to 92.5%. That indicates a 7.5–11%
prediction error rate related to occupants’ thermal comfort levels. [31]
2006 Predictive control system development for a
building heating system
Fuzzy + proportional–integral–differential (PID) controller
development for improving control performance
For a heater control, temperature increase times can be reduced from 12.7
sec to 4.3 sec; the settling time can be reduced from 16.3 sec to 6.9 sec;
overshooting can be reduced to 0%.
[32]
2006 Indoor thermal comfort controller development Fuzzy logic controller development
The measuring period was from 15 September 2004 until 17 September
2004 at a 2-sec sample rate. The indoor air quality was kept between
600–800 ppm. The predicted mean vote (PMV) fluctuates around one
[33]
2006 Cooling prediction of an existing HVAC
system in China
Combination of rough set (RS) theory and ANN for
cooling load prediction
The HVAC system has 11 air-handling units (AHU) and operates 24 h a
day. The prediction error rate of cooling energy during a 24-h period in
summer time ranged from 3.45% to 9.27%
[34]
2007 Hourly load demand forecast
Combining evolutionary program (EP) and particle swarm
optimization (PSO), combined with an artificial neural
network (CANN) was developed for short-term hourly
load forecasting
Hourly loads of a 6000-kW utility were predicted during the first week of
December 2005. Using the best trained CANN tool, MAPE can reach
2.24% to 3.25%.
[35]
2007 Achieving thermal comfort in two
simulated buildings
Development of a linear reinforcement learning controller
instead of using a traditional on/off controller
Controller development for saving energy while maintaining thermal
comfort; over a period of four years, the annual energy consumption
increased marginally from 4.77 MWh to 4.85 MWh. However,
the dissatisfaction index, predicted percentage of dissatisfied (PPD),
was decreased from 13.4% to 12.1%.
[36]
Sensors 2019,19, 1131 5 of 30
Table 1. Cont.
Year HVAC System AI Development Key Results Ref.
2008 Forecasting building energy consumption based
on simulation models and ANN
Comparison between detailed model simulations and ANN
for forecasting building energy consumption Difference between the detailed model and ANN is less than 2.1% [37]
2008 Predicting monthly heating loads of
residential buildings Regression model development for prediction MAPE ranges from 2.3% to 5.5% [38]
2008 Heat load prediction of a district’s heating and
cooling system
Recurrent neural network (RNN) development for heating
load prediction
During a four-month period in winter, daily prediction errors rates
ranged from 5.3% to 15.5% [39]
2009 Year-round temperature prediction of the
southeastern United States
Ward-type ANNs development for the prediction of air
temperature during the entire year based on
near real-time data
Using detailed weather data collected by the Georgia Automated
Environmental Monitoring Network, ANNs were trained to provide
prediction throughout the year. The prediction mean absolute error rate
(MAE) ranged from 0.516 C to 1.873 C
[40]
2009 Measuring the prediction performance of a wet
cooling tower
ANN development for the prediction of cooling tower
approaching temperatures The prediction means square error rate (MSE) of around 0.064 C [41]
2009
Control performance improvement of a typical
AHU variable air volume (VAV)
air-conditioning system
Model-based predictive control (MPC) development based
on a first-order plus time-delay model
For an air-conditioned area of about 1200 m2in Hong Kong, cooling air
can track the set point with an error rate of around 0.13 C[42]
2010 KBS applications in smart homes Autonomous caretaker to create an
environmentally-friendly and comfortable ambience Smart home ontology has the potential to save on labor costs [43]
2010 A chiller system in an intelligent building Optimization by RNN 7.4% energy savings [44]
2010 Intelligent multi-player grid management for
reducing energy cost Evolutionary computation development for cost saving 1 kwh of energy cost can be reduced from 0.773 to 0.313 .
Cost saving is around 62.4% [45]
2010
Fuzzy logic controller for greenhouse applications
Fuzzy controller design for universal purpose The controller can be used in any cultivation with different
environmental variables’ set points. [46]
2010 Prediction of heating energy consumption in a
model house at Denizli, Turkey Model-based prediction Prediction errors range from 2.3% to 5.5% [47]
2010
Prediction of annual heating and cooling loads for
80 residential buildings Model-based prediction Prediction errors range from 7.5% to 22.4% [48]
2011 Adaptive learning system at intelligent buildings Smart scheduling control based on deep learning 1.33 C shift close to occupants’ custom settings [49]
2011
Hybrid controller for energy management at a
simulated one-floor building of 128 m
2
, with a bay
window at the University of Perpignan Via
Domitia, south of France
Fuzzy-PID schema development for model predictive
control (MPC)
While maintaining thermal comfort, 1 C exceeding the set point can be
controlled to save 6% energy, but occupants will feel warm. PMV can be
ensured by an 0.2 C temperature increment. The energy saving is less
than 0.3%
[50]
2011 Predicting air outlet temperature of an indirect
evaporative cooling system
Soft computing tools include the fuzzy interference system
(FIS), ANN, and adaptive neuro fuzzy inference (ANFIS)
ANN trained by the Levenbergy–Marquardt algorithm provides the best
prediction performance. R2value can be as high as 0.9999. Predicted
temperature deviation is less than 1 C, and the error ranges from 1.1%
to 3.2%
[51]
Sensors 2019,19, 1131 6 of 30
Table 1. Cont.
Year HVAC System AI Development Key Results Ref.
2011 AI-based thermal control method for a typical US
single family house
ANFIS development and the control performance
comparison with ANN
ANFIS control can save 0.3% more energy than the ANN in the winter.
In the summertime, ANFIS can save 0.7% more energy [52]
2011
Predicting temperature and power consumption of
a district boiler Wavelet-based ANN development for accurate prediction Prediction errors range from 4.17% to 9.01% [53]
2011 Controller development for a heating and
cooling system GA-based fuzzy PID controller development Lowering equipment initial and operating cost up to 20% [54]
2011 Mining building performance data for
energy-efficient operation
Energy-efficient mining model development for predicting
environmental variables
The model is used to predict the environmental variables of a 4500 m2
south-facing low-energy building consisting of 70 rooms. The confidence
of room temperature prediction is 84.63%; that of radiant temperature
prediction is 90.34%; the CO2concentration prediction confidence is
64.68%; and that of relative humidity is 86.76%
[55]
2011
Regression model development for predicting
heating and cooling loads of buildings in
different climates
Principal components analysis (PCA) development for
predicting outdoor temperature Prediction errors range from 5.5% to 7.9% [56]
2012 Intelligent energy management system (EMS) for
smart offices
Distributed AI development for optimized scheduling
control of office equipment 12% energy saving [57]
2012
Cloud-based EMS and future energy environment
Distributed AI and machine to machine (M2M)
communication development 22.5% energy saving [58]
2012 Zone temperature prediction in buildings Predicting indoor temperature by traditional thermal
dynamic model, ANN, GA, and fuzzy logic approaches
MAE of prediction by traditional model is 0.422 C; ANN is 0.42 C;
GA is 0.753 C, and fuzzy logic is 0.741 C[59]
2012 Forecasting household electricity consumption RNN development for the short-term (one hour ahead)
forecasting of the household electric consumption
The house is located in a suburban area in the neighbors of the town of
Palermo, Italy. The prediction errors range from 1.5% to 4.6% [60]
2012
Model-based control of a HVAC system in a single
zone of a building
Multi-objective GA development for predicting air
temperature and relative humidity
MAE of temperature prediction is 0.1–0.6 C. Relative humidity is
0.5–3.0% [61]
2012 Coordinating occupants’ behaviors for building
energy and comfort management
Distributed AI development to achieve multi-agent
comfort management
Reducing 12% energy consumption while keeping thermal comfort with
the variation less than 0.5% [62]
2012 Optimization of chiller operation at the office
building of the company Imel in New Belgrade GA development for the optimization of chiller operation 2% energy saving during warmest summer days, and up to 13% during
the transition period at lower average external temperatures [63]
2012 Energy efficiency enhancement of a decoupled
HVAC system
Wavelet-based ANN development for optimization of
scheduling control
In mid-season operation, daily operation cost can be saved from 5.88%
to 11.16% [64]
2012 Hourly thermal load prediction Autoregressive with exogenous terms (ARX) model
development for thermal load prediction MAPE ranges from 9.5% to 17.5% [65]
2013
Multi-agent system (MAS) application in a
commercial building owned by Xerox Palo Alto
Research Center (PARC) in the US
MAS development for constructing a building comfort and
energy management system (BECMS)
Constructing a hierarchical function decomposition to provide
user solution [66]
Sensors 2019,19, 1131 7 of 30
Table 1. Cont.
Year HVAC System AI Development Key Results Ref.
2013
Three typical residential buildings with 3.3-kWp
photovoltaic (PV) plant located at Ripatransone
(AP), Italy
Radial basic function (RBF) network development for
monitoring home loads, detecting and forecasting PV
energy production and home consumptions, informs and
influences users on their energy choices
MAPE of home load prediction for next three hours is 9.70%, eight hours
is 12.20%, and 18 h is 16.30%.
MAPE of PV energy production for the next three hours is 7.70%,
eight hours is 9.30%, and 18 h is 11.80%.
[67]
[68]
2013 Smart homes in a smart grid
Supervisory control and data acquisition + house intelligent
management system = SHIM for charge and discharge of
the electric or plug-in hybrid vehicles, and the participation
in demand response (DR) programs
Considering the energy consumption data of a Portuguese house over
30 days in June 2012, the energy cost can be saved up to 12.1% [69]
2013 Designing customized energy service based on
disaggregation of heating usage Estimating heat usage by hidden Markov model (HMM) Heating usage can be predicted, and the errors range
from 4.64% to 8.74% [70]
2013 Using sensors commonly installed in office
buildings to recognize energy-related activities
Layered HMM development for recognizing
occupants’ behaviors
People counting can have the accuracy of 87% in the single-person room
and 78% in the multi-person room. The away and present activity can be
identified with the accuracy of 97.7% in the single-person room, but only
61% accuracy can be achieved in the multi-person room. The prediction
of other activities has accuracy ranges from 98.7% to 61%
[71]
2013 MAS for BECMS based on occupants’ behaviors User-oriented control based on behavior prediction Indoor thermal comfort is considered to be highly satisfactory to
occupants while maintaining a PMV of around 0.6065 [72]
2013 Predictive control of vapor compression
cycle system MPC development for multi-variable control
Energy saving by MPC can reach 25.31%. With the prediction by AI,
energy cost can be reduced up to 28.52%. Comparing the traditional
prediction by linear regression, energy-saving performance is improved
by 65.53% and cost-saving can be increased up to 63%
[72]
2013 A survey of energy-intelligent buildings based on
user activity
MAS for gathering real-time occupancy information,
predicting occupancy patterns and decision making Energy saving of HVAC equipment can reach 12% [73]
2013 Optimal energy management by load shift GA development for load shift control 35% load shift is possible under a reasonable storage capacity [74]
2014 Dynamic fuzzy controller development to meet
thermal comfort
ANN performs indoor temperature forecasts to deed a
fuzzy logic controller
Thermal comfort is very subjective, and may vary even in the same object
[75]
2014
Electricity demand prediction of the center of
investigation on energy solar (CIESOL)
bioclimatic building
Short-term predictive neural network model development With a short-term prediction horizon equal to 60 min, the mean
error is 11.48% [76]
2014 An autonomous hybrid power system PSO development for predicting weather conditions Techno-socio-economic criterion for the optimum mix of renewable
energy resources [77]
2014
Energy consumption prediction of a commercial
building that has a total floor area of 34,568 m2
and is located in Montreal, Quebec
Case-based reasoning (CBR) model development for
predicting following three-hour weather conditions and
indoor thermal loads
During occupancy, 07:00–18:00, the coefficient of variation of the root
mean square error (CV-RMSE) is below 13.2%, the normalized mean bias
error (NMBE) is below 5.8%, and the root mean square error (RMSE) is
below 14 kW
[78]
Sensors 2019,19, 1131 8 of 30
Table 1. Cont.
Year HVAC System AI Development Key Results Ref.
2014 Simulated 12 building types have the same
volume, ~771.75 m3SVR + ANN development for predicting heating and
cooling loads with eight input parameters
Prediction error is less than 4%. Compared with the traditional model
prediction, the SVR + ANN model can improve the prediction
error by 39.0%
[79]
2014 Intelligent energy management at 45 bus stations
at Alexandria
PSO development for occupancy prediction and the control
of renewable energy sources
During four-hour operation, power imported from the grid can be
limited by only 42% [80]
2014 93 households in Portugal ANN development for energy consumption and
load forecasting MAPE is 4.2% [81]
2014 AI development for estimating building
energy consumption
GA, ANN, and SVM development for building
estimation models
Peak difference in hourly prediction of different models can be as high as
90%. Monthly prediction is 40% and annual variation is 7% [82]
2014
Energy management optimization of a building
that has wooden external walls of 9 cm and a
wooden external roof of 9 cm.
Distributed AI development Distributed AI in the end control devices can save up to 39% energy
through the generation of optimal set points [83]
2015 Real-world application for energy savings in a
smart building at a Greek university
Rule-based approach development for optimized
scheduling control Daily energy saving can reach up to 4% [84]
2015 100 load curves in a smart grid ANN development for DSM Prediction error is less than 5.5% [85]
2015
Five AI algorithms conducted in a one-story test
building with a double skin; the building is 4.2 m
wide, 4.5 m deep, and 3.05 m high.
AI theory-based optimal control algorithm development for
improving the indoor temperature conditions and heating
energy efficiency
Compared with the transitional algorithm, this novel algorithm can
increase thermal comfort by around 2.27% [86]
2015 Solar combi-system combined with a gas boiler or
a heat pump ANN model development for predicting thermal load
Based on a learning sequence lasting only 12 days, the annual prediction
errors are less than 10% [87]
2015 Home energy management system in
25 households in Austria
Short-term smart learning electrical load algorithm
development to increase flexibility to fit more the generation
from renewable energies and micro co-generation devices
Prediction error is less than 8.2% [88]
2015 Three houses with wireless sensors for detecting
use occupancy and activity patterns
Non-linear multiclass SVM, HMM, and k-nearest neighbor
(kNN) model development to deal with the complex nature
of data collected from various sensors
AI algorithm development can increase 25% performance for predicting
occupants’ behaviors [89]
2015 Modeling for smart energy scheduling
in micro-grids
Operation policy and artificial fish swarm algorithm (AFSA)
for suggesting operation policy (scheduling control) of a
micro-grid with V2G (Vehicle to Grid)
5.81% energy cost saving [90]
2016 Hybrid renewable Energy systems AI development for tariff control 10% reduction of unit energy price [91]
2016 Model-based predictive control for building
energy management Model-based predictive controller development Set point optimization by occupants’ activities can save 34.1% energy [92]
2016
Multi-objective control and management for smart
energy buildings Hybrid multi-objective GA development
31.6% energy savings can be achieved for a smart building.
Compared with traditional optimization methods, thermal comfort can
be improved by 71.8%
[93]
Sensors 2019,19, 1131 9 of 30
Table 1. Cont.
Year HVAC System AI Development Key Results Ref.
2016
Hot water demand prediction model development
for residential energy management systems Bottom–up approach development
Total energy savings of 18.25%. Among them, 1.46% of that is attributed
to the use of AI tools, compared with linear-up prediction. [94]
2016 Hybrid forecasting model based on data
preprocessing, optimization, and AI algorithms AI-assisted data fusion MAPE ranges from 4.57% to 5.69% [95]
2017 Estimation of the energy savings potential in
national building stocks AI for analyzing user behaviors User-behavior trends were taken into account and up to a 10%
improvement of prediction accuracy resulted [96]
2017 Deep reinforcement learning for building
HVAC control Deep reinforcement learning (DFL)-based algorithm 11% energy savings [97]
2017 Office heating ventilation and air
conditioning systems
Reinforcement learning (RL) and long/short-term
memory RNN
2.5% energy savings while improving thermal comfort by an average
of 15% [98]
2018
Manager’s decision-making system for household
energy savings ANN-based decision making system (DMS) development Electricity bills could be reduced by around 10% [99]
2018 Energy consumption forecasting for building
energy management systems Elman neuro network Mean square error rate (MSE) ranges from 0.004413 to 0.005085 [100]
2018 Home air conditioner energy management and
optimization strategy with demand response MPC for demand response and air conditioning control 9.2% energy savings when compared to conventional On/Off control
and 1.8% energy savings compared with PID control [101]
2018 Non-linear control techniques for HVAC systems Fuzzy control Smoothly reaches to set point values. The steady state error rates range
from 0.2% to 3.3% [102]
2018 Enhancing building and HVAC system
energy efficiency MPC Most cases have an energy-savings rations range from 10% to 15% [103]
2018 Building air conditioning systems in micro-grids Distributed economic model predictive control (DEMPC) Predictions of energy prices are within 3% [104]
2018 HVAC systems at an office building
MAS and CBR for energy management and decision making
41% energy savings [105]
Table 1lists all the articles related to the application of AI technologies on the HVAC systems from 1997 to 2018 according to the PRISMA method. The results of the qualitative analysis of
Table 1are described in the following sections.
Sensors 2019,19, 1131 10 of 30
2. AI Developments and the Applications for HVAC Systems
In this study, keywords including AI, machine learning, heating, ventilation, and air conditioning,
were utilized to conduct a paper survey from the Science Direct on Line (SDOL), IEEE Xplore (IEL
Online), and MDPI databases. Initially, 737 papers were found from SDOL fitting the criteria of our
paper survey, while 34 were found from IEEE Xplore, and 12 were found from MDPI. After further
review, articles that were not related to HVAC control or methods to enhance performance were
separated out. A total of 79 articles fit the requirements of either (1) describing the applying factory;
(2) developing innovative AI tools and their use involving HVAC control; and (3) depictions describing
the overall performance of an HVAC system after applying AI control tools. These articles were chosen
for further exploration.
2.1. Study Case
The published year, HVAC system, developed AI technology, and key results of the collected
79 articles are listed in Table 1.
2.2. Developed AI Tools
In the second column of Table 1, there are 18 AI tools for HVAC systems. Among them, the most
well-known AI tools are neuro networks (NN), including artificial neuro networks (ANN), recurrent
neuro networks (RNN), spiking neuro networks (SNN), and wavelet ANN [
15
,
16
,
19
,
22
24
,
27
,
29
,
34
,
35
,
37
,
39
,
40
,
44
,
51
53
,
59
,
60
,
64
,
76
,
79
,
81
,
82
,
85
,
87
,
98
100
,
102
]. ANN is based on the nervous system,
the human brain architecture, and the learning processes. A set of interconnected neurons can be
separated into three layers, which are composed of input, output, and hidden layers. The HVAC
system inputs, network weights, and the transfer functions of the network lead to the output of ANN.
The ANN controller doesn’t need to identify the control model. The weight coefficient can be regulated
to minimize the costs. ANN can simulate the working procedure of the human brain; therefore,
it has the capability of having insight into a complex system. However, the brain-like controller has
disadvantages due to having to take a lot of time for off-line training as well as requiring a large
amount of data for the system to make quality predictions.
The second AI tool is used for the predictive control functions of ANN: fuzzy or model-based
predictive control (MPC) [
32
,
42
,
47
,
48
,
50
,
65
,
72
,
75
,
87
,
92
,
94
,
101
,
103
,
104
]. Predictive control provides
feedback of the results of the prediction to the system to allow for the adjustment of a system’s
control parameters. The predictive feedback system is different from previous control systems due to
the design of the feedback sensor. Collotta etc. created a non-linear autoregressive neural network
auto regressive external type (NNARX-type) structure in 2014 for indoor temperature prediction [
75
].
In addition to enhancing the control performance, the signal of a predictive control system could be
discontinuous for a non-linear system. This is different from the continuous signals that are needed for
a linear system managed by a traditional PID controller, which is based on the Laplace transform and
linear transfer functions. The insight ability of the ANN is similar to the human insight process, and is
a smart way to improve the performance of a non-linear system commanded by predictive control.
The third type of AI tool is known as distributed AI and the multi-agent system
(MAS) [
20
,
26
,
31
,
57
,
58
,
62
,
66
,
72
,
73
,
83
]. In addition to strengthening the entire performance of a system
using ANN or predictive control, the subsystems, sensors, and actuators of an HVAC system are able
to communicate and interact with each other and become an even more intelligent system through the
use of MAS.
The fourth type of AI tool is what is known as the genetic algorithm (GA) method, which is
based on biological evolution theory [
14
,
45
,
54
,
59
,
61
,
63
,
74
,
82
,
93
]. The GA method utilizes global
non-derivative-based optimization to tune the set points of HVAC systems and meet the thermal
comfort requirements without the use of a mathematical model of the system. However, the problem
Sensors 2019,19, 1131 11 of 30
with the GA method is that it requires massive calculations and long run times. Therefore, the GA
method might be inappropriate for the real-time operation of an HVAC system.
The fifth type of AI tools is employed for fuzzy control [
21
,
32
,
33
,
46
,
51
,
59
,
102
], support vector
machines (SVM), and R [
28
,
30
,
38
,
56
,
79
,
82
,
89
]. These two AI tools have the same amount of published
articles. A fuzzy logic controller (FLC) is similar to human reasoning and can be used to control a
complex system by using the rules of the IF–THEN algorithm. The utilization of fuzzy logic grades and
rules yields a low real-time response speed. This situation limits the application of the FLC onto HVAC
systems. However, SVM and R could be used in conjunction with the FLC for data classification by
finding the hard margins of various data sets to determine the proper control methodologies, modeling,
or regression for decision making. This method is used mainly for analyzing huge amounts of data,
modeling, and decision making, but is rarely used for HVAC system applications.
The seventh AI tools are model-based controls [
10
,
17
,
18
,
69
,
91
] and deep learning (DL,
or reinforced learning) [
36
,
49
,
88
,
97
,
98
]. The model-based control models, when used with the SVM
and R tools, collect and analyze data utilizing the distributed AI tool, and communicate and interact
with the MAS tool. The advantage of model-based control is its predictive strategy and high capability
of observation. However, the model-based control is a feedback control methodology that can only be
applied to a time-independent system. It can’t solve problems within a non-linear time-variable system.
A deep learning tool could determine a control strategy according to a system’s present conditions and
information from previous cases through a learning process without the use of modeling. Deep learning
is one of the broader machine learning methods, which is based on learning data representations,
as opposed to following task-specific algorithms. The learning types are supervised, semi-supervised,
or unsupervised. For an HVAC system, deep learning is a novel methodology to achieve more
intelligent control.
The knowledge-based system (KBS) [
11
13
,
43
] is similar to the DL tool. However, the difference
between them is that the DL tool is for controlling the system, and KBS is used for building various
SVM and R knowledge databases. KBS could provide an optimal control strategy for various HVAC
systems through the expert system. KBS and DL are mostly used for problem-solving procedures and
to support human learning, decision making, and actions. Another key tool is case-based reasoning
(CBR) [
78
]. However, there are not many published articles regarding this. CBR is able to analyze a
control strategy and provide the most optimal one in conjunction with KBS or model-based control
in certain cases. Nevertheless, KBS, DL, and CBR tools all need a large amount of data to learn from,
and will require a lot of time to collect the control data, which will increase initial installation costs.
In addition, there are some other AI tools worth mentioning, which include: particle swarm
optimization (PSO) [
35
,
77
,
80
] and the artificial fish swarm algorithm (AFSA) [
90
] for optimizing control
strategies, the hidden Markov model (HMM) [
70
,
71
,
89
] for modeling, radial basis function (RBF) [
67
,
68
]
for data collecting and analyzing, data combining technology [
94
,
95
], k-nearest neighbor (KNN) [
89
]
for analyzing the closest data attribute, and the autoregressive exogenous (ARX) technique [
65
] for
regression analysis with an external input and feedback control system.
2.3. AI Applications for HVAC Systems
The control methodologies of AI development can be observed by comparing columns one and
two of Table 1, which outline the AI tools and related HVAC systems, respectively. There are four main
HVAC system applications for AI tools, including (1) medium to large-scale utilities for commercial
buildings [
10
,
13
,
17
,
20
,
22
,
24
,
27
,
29
,
35
,
43
,
44
,
53
,
57
,
63
,
64
,
66
,
71
73
,
76
,
78
,
80
,
82
,
84
,
87
,
91
,
96
,
100
,
105
], (2) air
conditioners or chillers for residential buildings [
11
,
15
,
18
,
19
,
21
,
36
39
,
42
,
51
,
52
,
60
62
,
65
,
67
70
,
72
,
75
,
79
,
83
,
86
,
88
,
92
,
94
,
97
99
,
101
,
102
], (3) air conditioning systems for composite buildings [
25
,
28
,
30
,
34
,
40
,
45
,
50
,
54
,
56
,
58
,
59
,
74
,
77
,
81
,
85
,
90
,
93
,
95
,
103
,
104
], and (4) specific systems, such as a greenhouse, a regenerating
power system, a power system, etc. [12,14,16,23,26].
The use of AI tools applied onto commercial and residential buildings will be discussed, due to
the different occupant behavior patterns between the two building types. The occupants of commercial
Sensors 2019,19, 1131 12 of 30
buildings operate within the confines of working in the numerous companies within a commercial
building with a fixed office schedule, and therefore have more predictive air-conditioning demands.
The HVAC systems of most commercial buildings are operated by professional energy managers under
certain routines and energy-saving targets. Yet, the occupants of residential buildings, being residents,
have different air-conditioning behaviors and demands. In general, the HVAC systems of most
residential buildings are not operated by professional energy managers.
As mentioned in the previous section, ANN + fuzzy tools are the most widely utilized AI tools
for commercial and residential buildings. The adoption ratios for these two types of buildings are
34.5% (10/29) and 24.2% (8/33), respectively. The ANN tool can imitate the operating model of the
human brain to implement complex control strategies by learning and analyzing large amounts of data.
This is suitable for commercial buildings due to the predictive nature of the occupants. Unfortunately,
the ANN tool is not suitable for use in residential buildings. The ANN tool combined with DL,
reinforced learning, or deep reinforcement learning (DFL) equips the system with the capability of
feature extraction to analyze data and make control decisions, which replaces the need for a professional
energy manager.
For commercial buildings, CBR and KBS tools operate alongside ANN + fuzzy tools. CBR and KBS
tools can practice model base control and forecast several conditions, including weather, occupancy,
and energy consumption, optimize control set points, improve the energy efficiency of an HVAC
system, and ensure thermal comfort [
13
,
22
,
24
,
27
,
29
,
35
,
43
,
44
,
53
,
64
,
76
,
78
,
84
,
100
,
105
]. Based on the cases
utilizing ANN, CBR, and KBS tools, the ability to make predictions is the most significant function
of these AI tools. For residential buildings, DL, distributed AI, and MAS tools function alongside
ANN + fuzzy tools. If the fundamental devices of HVAC systems are equipped with distributed AI
tools for saving energy and ensuring the thermal comfort, and are able to interact with each other
through an MAS tool, then predictive control and the prediction of future environmental conditions
for enhancing a system’s overall performance could be achieved.
Finally, the most recent development of AI tools applied onto composite buildings is predictive
control [
50
,
103
,
104
], which improves the control performance of an HVAC system by having the ability
to make predictions. Composite building systems are a mix of residential commercial building systems.
3. Theoretical Analysis of AI Assisted HVAC Control
In this section, the control performance differences between typical HVAC controls and AI-assisted
HVAC controls are analyzed quantitatively. The control outputs were calculated by the common
analytic solutions of the AI-assisted HVAC controls in Table 1, which were then compared with those
of the on–off and proportional–differential–integral (PID) controls.
3.1. Typical HVAC Control
Typical HVAC controls for residential and commercial buildings utilize on–off and PID control
algorithms [
106
] in addition to sensor feedback controls to have the ability to control parameters
such as a system’s temperature, humidity, and ventilation. The controllable structure is presented
in Figure 1.
The control block diagram in Figure 1runs PID or an on–off algorithm by comparing the set point
values and sensor feedback values, and then providing the subsequent output control signals to an
HVAC system.
The on–off control output values are calculated according to the following Equation:
σ[S(t)SP](1 if S(t)SP >Threshold Value
0 if S(t)SP =0±Var[S(t)] (1)
where
σ
is the step function corresponding to the difference reading of the sensor feedback, S(t),
and the set point, SP, of an HVAC system. If the difference value is larger than the designed threshold
Sensors 2019,19, 1131 13 of 30
value, the value of
σ
is one. If the difference value of S(t) and SP is within the standard variation of
S(t), the value of
σ
is zero. The modification of on–off control is that, instead of being zero, the value of
σ
is located within the range of 0.5~0.7 when the difference value is within the standard variation of
S(t). This is the so-called floating control to avoid the large oscillation of a control signal of the HVAC
system. However, no matter how the typical on–off control or floating control is utilized, the final
control signal is determined by the difference of S(t) and SP, as shown in Equation (1).
Sensors 2019, 19, x FOR PEER REVIEW 17 of 33
an HVAC system, and ensure thermal comfort [13,22,24,27,29,35,43,44,53,64,76,78,84,100,105]. Based
on the cases utilizing ANN, CBR, and KBS tools, the ability to make predictions is the most significant
function of these AI tools. For residential buildings, DL, distributed AI, and MAS tools function
alongside ANN + fuzzy tools. If the fundamental devices of HVAC systems are equipped with
distributed AI tools for saving energy and ensuring the thermal comfort, and are able to interact with
each other through an MAS tool, then predictive control and the prediction of future environmental
conditions for enhancing a system’s overall performance could be achieved.
Finally, the most recent development of AI tools applied onto composite buildings is predictive
control [50,103,104], which improves the control performance of an HVAC system by having the
ability to make predictions. Composite building systems are a mix of residential commercial building
systems.
3. Theoretical Analysis of AI Assisted HVAC Control
In this section, the control performance differences between typical HVAC controls and AI-
assisted HVAC controls are analyzed quantitatively. The control outputs were calculated by the
common analytic solutions of the AI-assisted HVAC controls in Table 1, which were then compared
with those of the on–off and proportional–differential–integral (PID) controls.
3.1. Typical HVAC Control
Typical HVAC controls for residential and commercial buildings utilize on–off and PID control
algorithms [106] in addition to sensor feedback controls to have the ability to control parameters such
as a system’s temperature, humidity, and ventilation. The controllable structure is presented in
Figure 1.
Figure 1. Typical HVAC controls for residential or commercial buildings.
The control block diagram in Figure 1 runs PID or an on–off algorithm by comparing the set
point values and sensor feedback values, and then providing the subsequent output control signals
to an HVAC system.
The on–off control output values are calculated according to the following Equation:
σ[S(t)−SP] 1 if S(t)−SP>𝑇𝑟𝑒𝑠𝑜𝑙𝑑 𝑉𝑎𝑙𝑢𝑒
0if S
(t)−SP=0±Var[S(t)] (1)
where σ is the step function corresponding to the difference reading of the sensor feedback, S(t), and
the set point, SP, of an HVAC system. If the difference value is larger than the designed threshold
value, the value of σ is one. If the difference value of S(t) and SP is within the standard variation of
Figure 1. Typical HVAC controls for residential or commercial buildings.
The output of PID control, as shown in Figure 1, is calculated according to the following equation:
KP·[S(t)SP]+KI·Z[S(t)SP]dt +KD·d[S(t)SP]
dt (2)
where
KP
is the proportional constant,
KI
is the integral constant, and
KD
is the differential constant.
The differentiation between
S(t)SP
is able to predict the controlling oscillation of the next stage and
eliminate it within a short period. The integration of
S(t)SP
is capable of providing a stable output
of PID control and reaching the final state of S(t)SP 0 after a longer period.
3.2. AI-Assisted HVAC Control
The block diagram of AI-assisted HVAC control resulting from the collected articles is shown
in Figure 2.
The core of AI assisted HVAC control is the ANN tool illustrated as controller #1 in Figure 2.
The output, y, of the ANN tool is produced through many processes, or neurons, and these
neurons interconnect with each other by multiplying with the weights,
ω
, as shown in the following
equation [9,15,16,2224,27,29,44,47,51,52,64,75,81,85,87,99]:
y(x)=g n
i=0
ωixi!(3)
where
ω0
,
ω1
,
. . .
and
ωn
are the weighting coefficients, and g is a non-linear activation function,
which is usually a step or a sigmoid function, as illustrated by the following equation:
g(x)=1
1+eβxβ>0 (4)
The neuron output, y, is unidirectional both for feedback or feedforward control. The ANN tool is
skilled at solving data-intensive problems within the categories of pattern classification, clustering,
function approximation, prediction, optimization, content retrieval, and process control. It is similar to
the human ability to make a single decision based on multiple inputs. Therefore, the main characteristic
Sensors 2019,19, 1131 14 of 30
of AI-assisted HVAC control is its multiple sensor feedback, as shown in Figure 2. The multiple
feedback sensor collects several sensor inputs, including controllable and uncontrollable parameters,
to build a database. AI tools are not only in the central control port, as shown in controller #1 of
Figure 2, but they are also applied in the sensor port, as shown in controller #2 of Figure 2, for more
intelligent control.
Sensors 2019, 19, x FOR PEER REVIEW 18 of 33
S(t), the value of σ is zero. The modification of on–off control is that, instead of being zero, the value
of σ is located within the range of 0.5~0.7 when the difference value is within the standard variation
of S(t). This is the so-called floating control to avoid the large oscillation of a control signal of the
HVAC system. However, no matter how the typical on–off control or floating control is utilized, the
final control signal is determined by the difference of S(t) and SP, as shown in Equation (1).
The output of PID control, as shown in Figure 1, is calculated according to the following
equation:
K[S(t)−SP]+K∙[S(t)−SP]dt+Kd[S(t)−SP]
dt (2)
where K is the proportional constant, K is the integral constant, and K is the differential
constant. The differentiation between S(t)−SP is able to predict the controlling oscillation of the
next stage and eliminate it within a short period. The integration of S(t)−SP is capable of providing
a stable output of PID control and reaching the final state of S(t)−SP0 after a longer period.
3.2. AI-Assisted HVAC Control
The block diagram of AI-assisted HVAC control resulting from the collected articles is shown in
Figure 2.
Figure 2. AI-assisted HVAC controls for residential and commercial buildings.
The core of AI assisted HVAC control is the ANN tool illustrated as controller #1 in Figure 2.
The output, y, of the ANN tool is produced through many processes, or neurons, and these neurons
interconnect with each other by multiplying with the weights, ω, as shown in the following equation
[9,15,16,22–24,27,29,44,47,51,52,64,75,81,85,87,99]:
(x)=gωx
 (3)
where 𝜔, 𝜔, … and 𝜔 are the weighting coefficients, and g is a non-linear activation function,
which is usually a step or a sigmoid function, as illustrated by the following equation:
g(x)=1
1+e β >0 (4)
Figure 2. AI-assisted HVAC controls for residential and commercial buildings.
The most utilized intelligent control functions are the optimized setting and predictive control
functions, as shown in Figure 2. First, the optimized setting function utilizes the KBS [
11
13
,
43
,
67
,
68
,
84
]
or CBR [
34
,
78
,
105
] tools from the database block to determine the set point (SP). The similarity index
(SI) is employed during the calculation process, as shown in the following equation:
SIi=f
yic yip
MVi(5)
where
yic
and
yip
are the neuro outputs of the variable i for the control and past case, respectively.
MVi
is the mean difference of the variable i in the database. The function f maps the control case
to the whole case difference. Based on SI, the global similarity (GS) is calculated according to the
following equation:
GS =i(SIi×ωi)
iωi, i =1, 2, . . . , n (6)
where n is the number of the controlled case and ωiis the weighting coefficient.
The proportion Pjof the prediction from the past case j is:
Pj=GSj
GST, j =1, 2, . . . , m (7)
where
GST
is the sum of the global similarities between the selected m cases. Then, the optimized
setting point (SPopm) can be determined by the following equation:
SPopm =
jPj×SPj/N(j)(8)
where
SPj
is the set point of past case j. The optimized set point is determined from the built database,
including the previous controllable and uncontrollable parameters, and the desired SP value.
Sensors 2019,19, 1131 15 of 30
In addition to the optimized settings, other intelligent control functions are the predictive
controls, which utilize the ANN + fuzzy tool as the central controller, as shown in controller #1
of Figure 2. This tool employs an IF–THEN algorithm to enhance the control performance by
predicting the likelihood of future errors effectively and providing proper feedback. The SVM and R
tool [
28
,
30
,
38
,
56
,
79
,
82
,
89
] and autoregressive with exogenous terms (ARX) tool [
65
] are also suitable
for central and edge computing ports, respectively.
The first step of predictive control is to determine probability. After comparing the calculation
methods of several articles, the suggested equation is shown in the following:
Probi(t+1)=kθ[τi,k]α·[Si,k(t)]β
k[τi,k]α·[Si,k (t)]β
N(k)
(9)
where i indicates the ith sensor for detecting controllable or uncontrollable parameters.
Si,k(t)
is the ith
sensor value,
τi,k
is the pheromone intensity, and
α
and
β
are the experience parameters. In addition
to the probability value, a Guess value is also necessary for predictive control. It is calculated after the
ANN runs [9,15,16,2224,27,29,44,47,51,52,64,75,81,85,87,99] according to the following equation:
Guessi(t+1)=g
keθ
ωkSi,k(t)!(10)
where
ω0
,
ω1
,
. . .
and
ωn
are the weighting coefficients, and g is the non-linear activation function,
as illustrated above. The following equation is able to predict the sensor output of the next stage.
S(t+1)=a·S(t)+b·R1·
n
i=0
MAX[Probi(t+1)] +c·R2·
n
i=0
Guessi(t+1)(11)
where a is the momentum parameter, b is the self-influence parameter, and c is the measure insight.
R1and R2are the random numbers within [0,1] for predictive control.
3.3. Control Performance Index
The Harris index (H) and normalized Harris index (NHI) [
107
,
108
] are utilized for evaluating the
performances of typical and AI-assisted HVAC control outputs, as shown in the following equations:
H=lim
tηt=lim
t
V1
Var[y(t+1)] (12)
NHI = 1 1/H (13)
where
V1=Var[y|initial condition]
. The Harris index compares the variations between the initial
control y(0) and y(t + 1). There are several articles discussing the effect of rising time, settling time,
and overshooting [
32
] on the control performance of the linear system. However, the Harris index
and NHI are able to assess the performance of linear, non-linear, feedforward, and feedback control
systems [109], as well as thermal comfort and energy efficiency, etc.
4. Results and Discussions
In this study, the Harris index and NHI are employed to estimate the performance of HVAC
systems in Table 1managed by On–Off, PID, and AI-assisted control. The sensor signal outputs of the
On–Off and PID controls, as shown in Equations (1) and (2), have a positive linear relationship with
the Harris index. Therefore, the sensor types mentioned in the articles in Table 2will be indicated and,
then, the sensor errors will be calculated.
Sensors 2019,19, 1131 16 of 30
Table 2. Different sensors employed by AI-assisted HVAC control.
Year Academic Case AI Application Scenario Sensor Deployment Ref.
1997 Heating, ventilation, and air conditioning (HVAC) system
for improving occupant comfort and saving running costs Optimized setting
CO2sensor
Fire sensor
Occupancy sensor
Temperature sensor
[11]
2000 HVAC system with variable air volume (VAV) coils and
constant air volume (CAV) coils Predictive control
CO2sensor
Flow rate sensor
Pressure sensor
Humidity sensor
Temperature sensor
Volatile organic compounds (VOCs)
concentration sensor
[14]
2001 Optimal heating control in a passive solar commercial
building Optimized setting
Thermal comfort sensor module includes the ambient
temperature sensors and solar radiation sensors
Water temperature sensor
Energy consumption meter
[17]
2002 House_n demonstration at Massachusetts
Institute of Technology Optimized setting A fixed, wide-color camera, a microphone,
and a temperature sensor [18]
2003 Fuzzy controller development for energy conservation and
users’ indoor comfort requirements
Fuzzy control for improving control
performance
Hybrid sensor module consists of temperature
humidity, air velocity, CO
2
, mean radiant temperature
gauge, etc.
Outdoor temperature and humidity sensors
Indoor illuminance sensor
Indoor temperature sensor
Power meter
[21]
Sensors 2019,19, 1131 17 of 30
Table 2. Cont.
Year Academic Case AI Application Scenario Sensor Deployment Ref.
2003 Artificial neuro network (ANN) development for optimal
operation of heating system in building Predictive control Simulation based on temperature sensor data,
thermal resistances, and indoor heat gains [22]
2005 Predicting chiller energy consumption at a Laval building
operated from 7:30 to 23:00, Monday to Friday Model-based predictive control
Outdoor dry-bulb temperature sensor
Wet-bulb temperature sensor
Horizontal solar flux sensor
Status detector of chiller
Water temperature sensor
Flow meter
Electric power meter
[24]
2005 Internet-based HVAC system allows authorized users to
keep in close contact with a building automation system Optimized setting Web-enabled controller with pressure, temperature,
and flow sensors [25]
2006 Centralized HVAC system with multi-agent structure Distributed AI Simulation based on thermal comfort related sensors [31]
2006 Predictive control system development for a building
heating system Predictive control Temperature sensor [32]
2006 Indoor thermal comfort controller development
Fuzzy indoor thermal comfort
controller development by
simulation software
Simulation based on inputs from light sensor, outdoor
temperature sensor, relative humidity sensor,
air flow/hotwire anemometer, and CO2sensor [33]
2006 Cooling load prediction of an existing HVAC
system in China Load prediction Multiple sensor data input includes temperature,
relative humidity, and pressure [34]
Sensors 2019,19, 1131 18 of 30
Table 2. Cont.
Year Academic Case AI Application Scenario Sensor Deployment Ref.
2007 Linear reinforcement learning controller Machine learning and the adaptive
occupant satisfaction simulator
Three different configurations include:
Indoor temperature; outdoor temperature; relative
humidity; CO2
Indoor temperature; outdoor temperature; time; CO2
Indoor temperature; outdoor temperature; CO2
[36]
2008 Heating load prediction of a district heating
and cooling system Load prediction Temperature sensor
Weather meter [39]
2009 Controller development for a typical variable air volume
(VAV) air conditioning system Model-based predictive control
Pressure sensor
Temperature sensor
Humidity sensor
Flow station
CO2sensor
[42]
2010 Chiller development for an intelligent building Predictive control
and optimized setting
Temperature sensor
Power meter [44]
2011 Controller development for air conditioning system of
one-floor building Fuzzy PID
Temperature sensor
Relative humidity sensor
Solar radiation sensor
Power meter
[50]
2011 Thermal control of a typical US single family house
Fuzzy logic and adaptive neuro fuzzy
inference system (ANFIS) Temperature sensor [52]
2011 Controller development for a heating and cooling
energy system Predictive control Temperature sensor [54]
Sensors 2019,19, 1131 19 of 30
Table 2. Cont.
Year Academic Case AI Application Scenario Sensor Deployment Ref.
2012 Zone temperature prediction and control in buildings Predictive control and
optimized setting
Chilled water valve opening level
Chilled water flow rate sensor
Chilled temperature sensor
Outdoor temperature sensor
Indoor temperature sensor
[59]
2012
Model-based predictive control of HVAC systems for
ensuring thermal comfort and energy
consumption minimization
Predictive control and
optimized setting
Wireless sensor network with activity detector,
temperature sensor, humidity sensor, mean radiant
temperature sensor, doors/windows state detector
Weather station includes solar radiation, temperature,
and relative humidity
[61]
2012
Coordinating occupant behavior for saving energy
consumption of an HVAC system and improving
thermal comfort
Distributed AI
Real-world feedback data
Building/occupant data
Occupant suggestions [62]
2012 Optimization of chiller operation at the office building of
the Imel company in New Belgrade Optimized setting
The outlet temperature from the chiller (evaporator
outlet temperature sensor)
The return temperature sensor
The external temperature sensor
[63]
2012
Energy-efficiency enhancement of decoupled HVAC system
Wavelet-based artificial neuro
network (WNN)—Infinite impulse
response (IIR)—PID-based control
Temperature sensor
Humidity sensor
Air flow meter
Water flow meter
[64]
Sensors 2019,19, 1131 20 of 30
Table 2. Cont.
Year Academic Case AI Application Scenario Sensor Deployment Ref.
2013 Building energy and comfort management
system development Distributed AI
Sensors provide
Environmental data
Occupancy data
Energy data
[72]
2013 Energy intelligent building based on user activity
Distributed AI and predictive control
Wireless sensor networks include PIR sensors and magnetic
reed switch door sensor [73]
2013 Predictive control of a cooling plant Model-based predictive control Temperature sensor [71]
2014 Dynamic fuzzy controller Predictive control ANN forecasted parameters [75]
2014 Energy management optimization of a building Distributed AI
Indoor temperature sensor
Water temperature sensor
Supplied air flow rate meter
Inlet air temperature sensor
Motion sensor
[83]
2014 Optimal chiller loading problem solved by swarm
intelligence technique Optimized setting Power meter [85]
2015 AI theory-based optimal control for improving the indoor
temperature conditions and heating energy efficiency
Five control algorithms include
Rule + ANN
ANN + ANN
Fuzzy + ANN
ANFIS with two inputs + ANN
ANFIS with one input + ANN
Temperature sensor
Surface opening status detector [86]
Sensors 2019,19, 1131 21 of 30
Table 2. Cont.
Year Academic Case AI Application Scenario Sensor Deployment Ref.
2015 Three houses with wireless sensors for detecting use
occupancy and activity patterns
Optimized setting
and predictive control
Thermocouple array
Microphone
Hygro sensor
CO2and air quality detector
Ultrasonic sensor
[89]
2016 Model-based predictive control for the set point
optimization of an HVAC system Model-based predictive control
Temperature sensor
Building energy analysis model with heat and
moisture transfer through a wall [92]
2016
Multi-objective control and management of a smart building
Optimized setting
Temperature sensor
CO2concentration detector
Power meter [93]
2017 Deep reinforcement learning for building HVAC control Optimized setting Temperature sensor
Energy plus building model [97]
2018 AI enhanced air conditioning comfort by Ambi Climate Optimized setting
Temperature sensor
Humidity sensor
Sunlight sensor
Geolocation by users’ mobile phone
[110]
Sensors 2019,19, 1131 22 of 30
In addition, one commercialized product, Ambi Climate, with a geolocation sensor and applied
sensors for the academic cases are analyzed in Table 2. The sensor types and the individual sensor
errors are illustrated in Figure 3.
Sensors 2019, 19, x FOR PEER REVIEW 25 of 33
Power meter
2017
Deep reinforcement
learning for building
HVAC control
Optimized setting
Temperature
sensor
Energy plus
building model
[97]
2018
AI enhanced air
conditioning comfort
by Ambi Climate
Optimized setting
Temperature
sensor
Humidity sensor
Sunlight sensor
Geolocation by
users’ mobile phone
[110]
In addition, one commercialized product, Ambi Climate, with a geolocation sensor and applied
sensors for the academic cases are analyzed in Table 2. The sensor types and the individual sensor
errors are illustrated in Figure 3.
Figure 3. Sensor errors with respect to different type of sensors employed by AI-assisted HVAC
control.
The performance indexes of On–Off and PID controls are calculated by the sensor errors, as
shown in Figure 3. However, instead of sensor errors, the Harris indexes of the optimized settings
and predictive controls are determined by the predictive errors, as shown in Equations (8) and (11).
The collected prediction or forecast errors of AI-assisted HVAC controls in Table 1 are shown in
Figure 4.
3.00
2.77
4.00
7.17
0.50
15.00 14.50
1.50
0
2
4
6
8
10
12
14
16
18
20
Sensor error percentage (%)
Figure 3.
Sensor errors with respect to different type of sensors employed by AI-assisted HVAC control.
The performance indexes of On–Off and PID controls are calculated by the sensor errors, as shown
in Figure 3. However, instead of sensor errors, the Harris indexes of the optimized settings and
predictive controls are determined by the predictive errors, as shown in Equations (8) and (11).
The collected prediction or forecast errors of AI-assisted HVAC controls in Table 1are shown in Figure 4.
For the On–Off control variables of
V1
and
Var[y(t+1)]
, both are directly proportional to any
sensor errors. Therefore, the calculated H is equal to one, and it becomes the comparison reference.
For PID control, when the damping ratio is located in a lower damping ratio range from 0.5 to 1.5,
the
Var[y(t+1)]
is able to reduce sensor errors by up to 30%, which will in turn enhance the H index
value. Due to the reduction of the steady-state error by the integral (K
I
) control, a PID control has
a better control ability than that of an On–Off control system, when the damping ratio is located
within normal to lower value ranges. For higher damping ratio systems, the initial stage V1, the final
stage
Var[y(t+1)]
, and the NHI value of the PID control will fluctuate due to variations of the
proportional (K
P
) and differential (K
D
) control within a range of [0.2–0.69]. For AI-assisted HVAC
control, the
Var[y(t+1)]
is estimated from the sensor output S(t+1), and the assumption is that the
NHI is equal to one, as illustrated in Equations (12) and (13). However, the prediction or forecast errors
of the AI controls fluctuate at certain ranges and cause variations of the NHI. This occurs particularly
when the AI control utilizes human behavior algorithms or thermal comfort prediction algorithms,
and the NHI is even lower than that of the PID control. The NHIs of On–Off, PID, and AI-assisted
HVAC controls are shown in Figure 5.
Sensors 2019,19, 1131 23 of 30
Sensors 2019, 19, x FOR PEER REVIEW 26 of 33
Figure 4. Prediction or forecast errors of AI-assisted HVAC control.
For the On–Off control variables of V and Var[y(t+1)], both are directly proportional to any
sensor errors. Therefore, the calculated H is equal to one, and it becomes the comparison reference.
For PID control, when the damping ratio is located in a lower damping ratio range from 0.5 to 1.5,
the Var[y(t+1)] is able to reduce sensor errors by up to 30%, which will in turn enhance the H index
value. Due to the reduction of the steady-state error by the integral (K
I
) control, a PID control has a
better control ability than that of an On–Off control system, when the damping ratio is located within
normal to lower value ranges. For higher damping ratio systems, the initial stage V, the final stage
Var[y(t+1)], and the NHI value of the PID control will fluctuate due to variations of the proportional
(K
P
) and differential (K
D
) control within a range of [0.2–0.69]. For AI-assisted HVAC control, the
Var[y(t+1)] is estimated from the sensor output S(t+1), and the assumption is that the NHI is equal
to one, as illustrated in Equations (12) and (13). However, the prediction or forecast errors of the AI
controls fluctuate at certain ranges and cause variations of the NHI. This occurs particularly when
the AI control utilizes human behavior algorithms or thermal comfort prediction algorithms, and the
NHI is even lower than that of the PID control. The NHIs of On–Off, PID, and AI-assisted HVAC
controls are shown in Figure 5.
Figure 5. Normalized Harris index (NHI) of different kinds of HVAC controls and the expected
performance improvements for energy savings.
7.46
14.50
3.50
9.98
0
5
10
15
20
25
30
Energy
consumption/
heating/ cooling
prediction
Human
behavior/
thermal comfort
prediction
Weather
forecast
Others
Prediction error percentage (%)
Figure 4. Prediction or forecast errors of AI-assisted HVAC control.
Sensors 2019, 19, x FOR PEER REVIEW 26 of 33
Figure 4. Prediction or forecast errors of AI-assisted HVAC control.
For the On–Off control variables of V and Var[y(t+1)], both are directly proportional to any
sensor errors. Therefore, the calculated H is equal to one, and it becomes the comparison reference.
For PID control, when the damping ratio is located in a lower damping ratio range from 0.5 to 1.5,
the Var[y(t+1)] is able to reduce sensor errors by up to 30%, which will in turn enhance the H index
value. Due to the reduction of the steady-state error by the integral (K
I
) control, a PID control has a
better control ability than that of an On–Off control system, when the damping ratio is located within
normal to lower value ranges. For higher damping ratio systems, the initial stage V, the final stage
Var[y(t+1)], and the NHI value of the PID control will fluctuate due to variations of the proportional
(K
P
) and differential (K
D
) control within a range of [0.2–0.69]. For AI-assisted HVAC control, the
Var[y(t+1)] is estimated from the sensor output S(t+1), and the assumption is that the NHI is equal
to one, as illustrated in Equations (12) and (13). However, the prediction or forecast errors of the AI
controls fluctuate at certain ranges and cause variations of the NHI. This occurs particularly when
the AI control utilizes human behavior algorithms or thermal comfort prediction algorithms, and the
NHI is even lower than that of the PID control. The NHIs of On–Off, PID, and AI-assisted HVAC
controls are shown in Figure 5.
Figure 5. Normalized Harris index (NHI) of different kinds of HVAC controls and the expected
performance improvements for energy savings.
7.46
14.50
3.50
9.98
0
5
10
15
20
25
30
Energy
consumption/
heating/ cooling
prediction
Human
behavior/
thermal comfort
prediction
Weather
forecast
Others
Prediction error percentage (%)
Figure 5.
Normalized Harris index (NHI) of different kinds of HVAC controls and the expected
performance improvements for energy savings.
The NHI is utilized to evaluate the performance of the control tools, and especially focuses on
the energy-saving percentages, because of its capability to estimate the performance of linear and
non-linear control systems. In Table 1, there are only 24 cases [
11
,
12
,
14
,
19
,
21
,
44
,
50
,
57
,
58
,
62
,
63
,
72
,
73
,
83
,
84
,
92
94
,
97
,
98
,
101
,
103
,
105
] that have references to the energy-saving percentages of AI-assisted
HVAC controls. The average energy saving percentages of these 24 cases are shown in Figure 6, and a
maximum energy savings of 41% is achieved by decision making through the MAS and CBR tools.
In Figure 6, the average energy savings percentage when using AI-assisted HVAC control is
14.02%. Of the 24 cases, 83% were comprised of On–Off control, and 17% were comprised of PID
control. Based on the NHI, the estimated average energy savings percentage, variations in energy
savings, and the maximum energy savings of AI-assisted HVAC control are 14.4%, 22.32%, and 44.04%,
respectively. Comparing these results with the experimental data of 14.02%, 24.52%, and 41.0% in
Figure 6, the errors are 3%, 9%, and 7%, respectively.
Sensors 2019,19, 1131 24 of 30
Sensors 2019, 19, x FOR PEER REVIEW 27 of 33
The NHI is utilized to evaluate the performance of the control tools, and especially focuses on
the energy-saving percentages, because of its capability to estimate the performance of linear and
non-linear control systems. In Table 1, there are only 24 cases
[11,12,14,19,21,44,50,57,58,62,63,72,73,83,84,92–94,97,98,101,103,105] that have references to the
energy-saving percentages of AI-assisted HVAC controls. The average energy saving percentages of
these 24 cases are shown in Figure 6, and a maximum energy savings of 41% is achieved by decision
making through the MAS and CBR tools.
Figure 6. The average energy savings of the 24 cases and the maximum energy savings achieved by
AI-assisted HVAC control.
In Figure 6, the average energy savings percentage when using AI-assisted HVAC control is
14.02%. Of the 24 cases, 83% were comprised of On–Off control, and 17% were comprised of PID
control. Based on the NHI, the estimated average energy savings percentage, variations in energy
savings, and the maximum energy savings of AI-assisted HVAC control are 14.4%, 22.32%, and
44.04%, respectively. Comparing these results with the experimental data of 14.02%, 24.52%, and
41.0% in Figure 6, the errors are 3%, 9%, and 7%, respectively.
5. Conclusions
The presented NHI in this research can be used to evaluate the performance of AI-assisted
HVAC control effectively, especially for non-linear control systems assisted by the optimized setting
with CBR or KBS tools, or predictive control with the distributed AI and fuzzy algorithm. In order to
calculate the NHI, the following hypotheses are made:
(1) If the prediction/forecast accuracy could reach 3.5%, which approaches the thresholds of weather
forecast accuracy and the accuracies of several types of sensors, including the thermistor, chip
type temperature sensor, and humidity sensor, the performance of AI-assisted HVAC control
will be enhanced. When compared with the On–Off and PID control strategies, the performance
of the AI-assisted HVAC control had an increase of 57.0% and 44.64%, respectively. The increased
energy saving percentages are above the average, and even above the maximum energy savings
that were found in any of the published articles from 1997 to 2018.
(2) In this study, the lower accuracy of the prediction tools and the resulting poor energy savings of
HVAC systems are hypothesized. This hypothesis is from the collected articles, and forms the
qualitative research in this paper. In the future, based on the hypothesis, the performance
improvement of AI-assisted HVAC control will depend on the prediction accuracy of the sensors,
which will be evidenced through the numerical simulation in Part 2 and the confirming
experiments in Part 3.
14.02
41.00
10.50
NA
0
10
20
30
40
50
AI assisted HVAC control Max energy saving by AI assisted
HVAC control
Energy saving percentage (%)
Averaged saving effect Uncertainty
Figure 6.
The average energy savings of the 24 cases and the maximum energy savings achieved by
AI-assisted HVAC control.
5. Conclusions
The presented NHI in this research can be used to evaluate the performance of AI-assisted HVAC
control effectively, especially for non-linear control systems assisted by the optimized setting with CBR
or KBS tools, or predictive control with the distributed AI and fuzzy algorithm. In order to calculate
the NHI, the following hypotheses are made:
(1) If the prediction/forecast accuracy could reach 3.5%, which approaches the thresholds of weather
forecast accuracy and the accuracies of several types of sensors, including the thermistor, chip type
temperature sensor, and humidity sensor, the performance of AI-assisted HVAC control will
be enhanced. When compared with the On–Off and PID control strategies, the performance of
the AI-assisted HVAC control had an increase of 57.0% and 44.64%, respectively. The increased
energy saving percentages are above the average, and even above the maximum energy savings
that were found in any of the published articles from 1997 to 2018.
(2)
In this study, the lower accuracy of the prediction tools and the resulting poor energy savings of
HVAC systems are hypothesized. This hypothesis is from the collected articles, and forms the
qualitative research in this paper. In the future, based on the hypothesis, the performance
improvement of AI-assisted HVAC control will depend on the prediction accuracy of the
sensors, which will be evidenced through the numerical simulation in Part 2 and the confirming
experiments in Part 3.
(3)
The existing sensors are designed for accurate sensing, but not for accurate prediction, and this
causes an unmet demand of the sensors. Improved sensors for AI-assisted HVAC controls should
be able to provide the ability of more accurate prediction. Based on Bayes’ theorem, accurate
prediction depends on the conditional probability. The priori probability can be utilized to
determine the posterior possibility, and the consistent prediction can be achieved by aggregation.
The priori information notice (PIN) design for sensors are provided in this study to decrease the
prediction errors to as low as 3.5% or less. The details of the PIN sensor will be discussed in Part 2
of the serial research.
Sensors 2019,19, 1131 25 of 30
Author Contributions:
D.L. initiated the idea, provided the draft, and completed the theoretical calculation.
C.-C.C. completed the writing, translation, and revision of the paper.
Funding:
This research was funded by Ministry of Science and Technology, R.O.C. through the contract MOST
106-2622-E-027-025-CC2 and 107-2622-E-027-001-CC2, and the industrial cooperating project between the National
Taipei University of Technology (NTUT) and Hitachi Taiwan Company.
Conflicts of Interest: The authors declare no conflict of interest.
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... Similarly, Evins R. [11] reviewed research works on the application of computational optimization to address issues in sustainable building design. Behrooz F. et al. [12] investigated control techniques for heating, ventilation and air conditioning and refrigeration (HVAC & R) control by insisting on a Fuzzy Cognitive Maps (FCM) as an intelligent method, whereas Cheng C.-C. and Lee D. [13] covered the techniques of artificial intelligence to optimize HVAC control. Royapoor M. et al. [14] discussed the industrial perspectives of building control techniques, while Qolomany B. et al. [15] discussed the role of machine learning techniques and big-data analytics in smart building services. ...
... Cheng C.-C. And Lee D. [13] 2019 Cover the role of AI technology to improve the efficiency of HVAC systems with discussing the AI-assisted techniques accuracy in data prediction. ...
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