Dynamic evaluation method to increase the effect of the automation system on the building energy performance

Journal Pre-proof Dynamic evaluation method to increase the effect of the automation system on the building energy performance Hatice Sozer, Fatih Tuysuz PII:

S0959-6526(19)34681-5

DOI:

https://doi.org/10.1016/j.jclepro.2019.119811

Reference:

JCLP 119811

To appear in:

Journal of Cleaner Production

Received Date: 15 March 2019 Revised Date:

25 November 2019

Accepted Date: 19 December 2019

Please cite this article as: Sozer H, Tuysuz F, Dynamic evaluation method to increase the effect of the automation system on the building energy performance, Journal of Cleaner Production (2020), doi: https://doi.org/10.1016/j.jclepro.2019.119811. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.

Hatice Sözer: Supervision, Validation, Conceptualization, Methodology, Writing- Reviewing and Editing Fatih Tüysüz: Simulations, Writing- Original draft preparation.

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DYNAMIC EVALUATION METHOD to INCREASE THE EFFECT of the AUTOMATION SYSTEM on the BUILDING ENERGY PERFORMANCE A Case Study of a Big Scale Residential Building Hatice SOZER, Fatih TUYSUZ Energy Institute, Istanbul Technical University, Istanbul, Turkey

ABSTRACT

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In this paper, an energy performance model of a residential building, including the heating

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and cooling systems, is created with the integration of an automation system. The aim is to

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identify the effect of the building automation systems on the heating and cooling energy

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consumption by controlling the working scheme of building systems.

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A methodology is developed to integrate the diverse building systems such as heat pumps,

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boiler, and solar collectors with their operational arrangements through an automation system.

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Three different control scenarios are investigated for the comparison by utilizing different

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software. The building energy model is prepared with TRNSYS while scenario conditions are

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written in MATLAB to import into the TRNSYS via a specific component which is

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performed as a bridge between MATLAB and TRNSYS. Subsequently, a dynamic hourly

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simulation for altering the setpoint values for indoor thermal conditions is integrated by

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application of Fuzzy logic toolbox from MATLAB.

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The first scenario is the Base Case (BC) which has a simple working principle. The

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operational structure is based on if/then relation. As a result, the energy consumption for

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heating and cooling are 88.16 kWh/m2 and 21.57 kWh/m2 respectively. The second scenario

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is called the application of Information and Communication Technology (ICT), which has

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more complex conditions. The last scenario, a dynamic hourly simulation which was

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performed by changing the setpoint temperature values within a specified range instead of

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using fixed seasonal setpoints. The aim was to improve energy efficiency while providing

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comfort conditions in the building with the dynamic setpoints and get more accurate results.

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The analysis shows that the consumptions are decreased 10.37% to 82.71 kWh/m2 while

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4.46% is only from the dynamic setpoint changes for heating, and 14.88% to 20.26 kWh/m2

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while 9.39% % is only from the dynamic setpoint changes for cooling.

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Keywords: Automation systems, Mechanical systems, Solar thermal heat pumps, Dynamic simulation, Energy performance, and Energy efficiency.

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Abbreviations:

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ASHP: Air Source Heat Pump COP: Coefficient of Performance DSHP: Dual Source Heat Pump EF: Energy Factor GSHP: Ground Source Heat Pump HAGHE: Horizontal Air-Ground Heat Exchanger HP: Heat Pump IAE: International Energy Agency ICT: Information and Communication Technology IEA: International energy agency KTOE: Kilotons of Oil Equivalent LCA: Life Cycle Assessment MPC: Model Predictive Controls PI: Proportional Integral RBC: Rule-based controls PV/T: Solar Photovoltaic/Thermal SASHP: Solar Air Source Heat Pump SGSHP: Solar Ground Source Heat Pump SPF: Seasonal Performance Factors ST: Collector Solar Thermal W&ASHP: Water & Air Source Heat Pump WSHP: Water Source Heat Pump

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1. INTRODUCTION

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Global warming is becoming a serious problem around the worldwide. Relatedly, global

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energy consumption is getting higher day by day with the development of countries' industries

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and economies. The total final energy consumption of the world is increased from 6,270,990

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KTOE (in 1990) to 9,555,323 KTOE (in 2016) according to International Energy

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Agency(IAE, 2016). This corresponds to an increase of approximately 52% in 26 years, and

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this increase will progress. Besides, the distribution of these consumptions by sectors provide 2

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a better understanding of where to focus on reducing this increasing energy consumption.

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Again when the IEA’s the year of 2016 data are considered, industry, transport, residential,

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and the others cover 31.7%, 31.6%, 21.6%, and 15.1% respectively [IAE, 2016]. Even, the

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industry and transport are top-notches, residential has a considerable amount that might be an

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excellent choice to concentrate on reducing energy consumption, as aimed in this study.

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There are many approaches to decrease the energy consumption of the residential. These

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could be sorted as adding or enhancing insulation, reusing waste heat, using renewable energy

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systems, improving the efficiency of mechanical systems, integration of automation systems,

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and so on. As one of those alternatives, the integration of efficient mechanical systems such

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as heat pumps with the combination of solar panels and automation systems have significant

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impacts on energy consumption of the residential as being investigated in many studies in the

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literature.

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1.1.

Improvement of energy efficiency through the application of solar assisted heat pump systems and importance of their modeling

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There is various research about improving the buildings’ energy performance with efficient

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mechanical systems and further utilizing the renewable systems. Most of those also have

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indicated the importance of modeling the systems during the design process to calculate their

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efficiency accurately.

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Mohanraj et al. had published two papers to identify the solar assisted heat pump systems and

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their applications. In the first paper, they have assembled a comprehensive research in terms

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of the system configuration, modeling, performance, and modifications (Mohanraj et al.-A,

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2018). In the second paper, they have classified these systems in terms of their usage; drying,

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room space heating, agricultural greenhouse space heating, water heating, and desalination

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applications. Then these applications have explained in detail As a results, they have

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remarked the importance of the detailed configuration of the systems before making decision

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about their applications (Mohanraj et al. -B, 2018)

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Genkinger et al. (Genkinger et al., 2012) investigated the air-to-water heat pumps combined

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with solar thermal collectors and photovoltaics for domestic hot water production in

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Switzerland. They have developed a LCA model to evaluate these two systems from different

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perspectives; ecological and financial aspects. The results showed that both combined systems

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have a similar economic and environmental effect.

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Fraga et al. (Fraga et al., 2012) further integrated the monitored data in their evaluation. They

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have monitored an existing heat pump and solar collector system, which is used for both

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heating and domestic hot water production to a large-scale complex (nearly 10,000 m2) to

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investigate the behavior of the system and to calculate the system Coefficient of Performance

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(COP). The monitoring was applied only in one of the ten buildings. As a result, they had

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better accuracy with monitored results that the heating demand (nearly 20kWh/m2/year) was

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lower while the domestic hot water demand (almost 35 kWh/m2/year) was higher compared to

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the Swiss standards. Besides, system COP was in between 1.7-5.6.

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Eicher et al. (Eicher et al., 2012) studied solar system integration on the heat pump’s (HP)

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evaporator part to maximize the performance of the system. Both test bench measurements

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and dynamic simulations (TRNSYS 16) were used to investigate the performance of the

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system.

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Lerch et al. (Lerch et al., 2014) investigated different combinations of solar thermal and heat

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pump systems by using dynamic system simulations in TRNSYS. Six different solar thermal

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heat pump systems were examined and compared. Three different building types were

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selected to set boundary conditions, then behaviors of these heating systems on one of the

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selected buildings were shown. As a result, the seasonal performance factor of the system was

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increased from 2.55 to 3.65 by adding a solar thermal system to heat pump. By preheating the

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ambient air at the outdoor unit of the HP were increased the system Seasonal Performance

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Factors (SPF) from 3.65 to 3.68. Also, results showed that SPF could be increased with

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additional ice storage. Carbonell et al. numerically analyzed the solar thermal systems with

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heat pumps for different climates around Europe by using Polysun-6 software. According to

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results of this study, the performance of the ground source heat pumps increased when a solar

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system was added. On the other hand, the performance of the air source heat pumps decreases

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when a solar system was added. Because of this, potential electricity savings of ground source

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heat pumps were higher than air source heat pumps (Carbonell et al.-A, 2014). Also, in

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another study, TRNSYS and PolySun-6 were compared in detail by Carbonell et al.

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(Carbonell et al.-B, 2014). In general, differences between these two simulation tools in the

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HP and SPF were up to 4% for Air Source Heat Pump (ASHP) systems and up to 14% for

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Ground Source Heat Pump (GSHP) systems.

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Zhu et al. studied solar water source heat pumps for buildings in three different cities to see

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the load characteristics in dissimilar climate regions by using eQuest and TRNSYS software.

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As a conclusion of this study, the three different climate regions were evaluated under four

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headings; feasibility, energy-saving property, economy, and environmental protection

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property. Severe cold regions were the most appropriate one in feasibility and energy-saving

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while hot summer and cold winter regions were the first in the economy. (Zhu et al., 2015)

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On the other hand, Buker et al. have remarked the complexity of the system modelling in

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terms of data collection. They have stated that having a variety of configurations, parameters

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and performance criteria may lead to a major conflict. They have investigated solar assisted

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heat pump systems for low-temperature heating applications by providing an advance reviews

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about direct and indirect series systems, system components, efficiencies and COP (Buker et

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al., 2016). Baglivo et al. investigated air-cooled heat pumps coupled with Horizontal Air-

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Ground Heat Exchanger (HAGHE) to see the performances of the systems with and without

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HAGHE by using TRNSYS 17 software. According to this study, in the winter period, the

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combined system (with HAGHE) showed acceptable COP values until February, in March it

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lost its effect, so the use of HAGHE had to be by-passed in March. On the other hand, in the

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summer period, the combined system always had higher energy efficiency values than without

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HAGHE system (Baglivo et al, 2017).

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Yin et al. analyzed an air-source heat pump combined with solar heating and thermal storage

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by defining the optimal operation strategy. The purpose of this study was to maximize the

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overall efficiency of the system. The results of this study showed that; overall energy

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efficiency of the system were decreased when the solar radiation and ambient temperature

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were decreased. Also, electricity consumption could be reduced by 31%. The optimal

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operation type was: during day time, solar heating system was activated and hot water was

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stored in the tank; during night time, water tank release the heat and air-source heat pump

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works (Yin et al.,2017).

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Emmi et al. compared ground source heat pumps with air source heat pumps and a standard

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plant system using a gas boiler for heating and air-to-water chiller for cooling in two different

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buildings located in Italy. According to this study, the GSHP system was always the best

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solution from the primary energy point of view (Emmi et al., 2017).

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Jonas et al. made a study about the ground and air heat pumps with solar thermal systems and

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used TRNSYS to get simulation results of these different combined systems. This study

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showed that SPF increases with increasing ratio of Solar Thermal (ST) collector area and is

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higher for Solar Ground Source Heat Pump (SGSHP) systems than Solar Air Source Heat

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Pump (SASHP) systems. For Strasbourg climate, SPF of SGSHP was between 0.5-1.1 higher

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than SPF of SASHP. For Helsinki climate, SPF of SGSHP-P was between 1.0-2.0 (Jonas et

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al., 2017).

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Wang et al. designed a solar photovoltaic/thermal (PV/T) heat pump system which had a

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heating mode in winter, the cooling mode in summer, domestic hot water heating and

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production of electricity for the building. Besides, seven different modes for heating, cooling,

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power production, and water heating were defined, analyzed, and compared. The results

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demonstrated that PV/T-Water Source Heat Pump (WSHP) heating mode and PV/T-

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W&ASHP heating mode had COPs of 3.18 and 2.53, which was higher than ASHP COP

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(Wang et al.,2018).

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Li et al. created three different solar thermal heat pump models in TRNSYS to see which

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system offers better energy consumption, energy utilization, and COP in the winter season.

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Additionally, the practical operation of the solar thermal heat pump system in the winter

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season in an office building was monitored for one day, and the results demonstrated that the

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COP of that day was 5.2 (Li et al.,2018).

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Qian has constructed a solar-powered GSHP by using GSHP, solar PV panels, batteries,

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converter, charge controller, and additional stuff. Monitoring and data acquisition system

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were used to get instant data from different sensors that installed different locations on the

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system. Monitoring was done for four weeks. Moreover, a model was created with Modelica

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software, and simulation results were compared with the on-site measurements. Results

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demonstrated that actual measured produced energy from solar panels was 242 MJ and

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theoretical was about 297 MJ. According to simulation results, the COP of the system was

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around 2.9 when the system was in steady-state (Qian, 2017).

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Lotz investigated the performance of the heat pump assisted solar thermal system. For this

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purpose, a dashboard was created that shows the collected data from sensors and calculated

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system performance metrics. These calculated metrics were overall energy factor, solar

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energy factor, heat pump energy factor, total energy consumption/collection/delivered loads,

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and heat delivery efficiency. Monitoring period of the system was between February 29th and

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March 28th, 2016, but the testing period of the system lasted the last two weeks of the given

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period. Consequently, solar Energy Factor (EF), heat pump EF, and overall EF calculated by

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dashboard were compared with the manual calculated results to evaluate the accuracy of the

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energy dashboard algorithm. The errors of the solar EF, heat pump EF and overall EF were

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1.7%, 0.8%, and 0.8% respectively. According to the dashboard, energy factors of solar, heat

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pump, and overall were 26.95, 1.25, and 2.29 respectively (Lotz, 2016).

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1.2.

Improvement of energy efficiency through the application of Control Strategies

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Grossi et al. have investigated the operation of a dual-source heat pump in different modes

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such as; air source, ground source, and dual source. A Proportional Integral (PI) control

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strategy was used to treats the heat pump as a ground or air source heat pump. It was based on

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supply water temperature. The setpoint value of the supply water temperature was set to 45 ˚C

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in heating mode and 7 ˚C in cooling mode. The on-off logic worked based on a dead band of

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5K centered on the setpoint value. When the external air temperature was lower than the

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defined temperature, then the Dual Source Heat Pump (DSHP) changed its operating mode

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from air to ground-source mode (Grossi et al.,2017).

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Potočnik et al. recently studied on analysis and optimization of a weather-controlled air-to-

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water heat pump by using TRNSYS and MATLAB. Six different cases were defined, and the

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results of these cases were compared. According to this study, it was observed that the

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addition of solar radiation input as an additional factor to the temperature improved the results

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(Potočnik et al., 2018).Péan et al. prepared a study about control strategies of the heat pump

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systems for improving energy flexibility. Rule-based controls (RBC) and Model predictive

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controls (MPC) were the two main control strategies classified and explained in the study.

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The principle of most of the rule-based control strategies was that a parameter was monitored

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and according to the monitoring process, the heat pump was started or stop. Even though rule-

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based controls could serve significant improvements, the MPC strategy served better results

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(Péan et al., 2018).

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Weeratunge et al. have examined two different types of solar assisted ground source heat

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pump and three different modes. In the first type, it was used the ground as thermal storage,

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and in the second one, there was an additional insulated hot water tank to store the water. The

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three control modes were that; set point (baseline), min-consumption, and min-cost.

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According to results, system 2 had the lowest electricity consumption for the coldest month

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(Weeratunge et al., 2018).

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Li et al. have used Taguchi optimization to compare the performance of a single tank and dual

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tank solar thermal heat pumps in five different climatic conditions. Three control factors were

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determined for a single tank system, and four control factors were determined for a dual tank

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system. As a result, it was observed that each factor had different effects on different climatic

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conditions. However, for all climatic conditions, the flow rate of the heat pump was the most

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influential factor for single tank system, on the other hand, the flow rate of the solar collector

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was the most influential factor for dual tank system (Li et al.,2018).

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Degrove have analyzed the workings of the solar thermal heat pump assisted hydronic system.

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The control system of the heat pump system had 28 inputs and 13 outputs in total. Also, it had

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seven different modes which were solar preheat mode, heat pump mode, hybrid mode, solar

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mode, solar dissipation mode, solar storage mode, and system off mode. The system was

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monitored from February 25th to March 13th. According to results, the system produced about

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205,000 Wh worth of thermal energy from the heat pump and solar collectors and 35.2% of

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the total heat gained was contributed from the solar thermal collectors. Also, results

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demonstrated that the system was off mode with 58%, solar storage mode with 16%, heat

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pump mode with 10%, and the remaining modes ranged from 3% to %7 (Degrove, 2015).

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Up to now, research that are related to solar thermal heat pumps, and automation systems

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were summarized. As investigated, most of the literature has been focused on the efficiency,

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selection and integration process of the systems that were investigated individually, apart

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from the other building specifications.

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1.3.

Contribution of this research to existing literature

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This research further provides two distinctive contributions that were evaluated to improve

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the accuracy of building energy modeling and performance. First one, the system was

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modeled and evaluated within the building. A divers system as heat pumps with its connection

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to boilers, and solar collectors was designed with the consideration of all building’s

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specifications. Therefore, whole system efficiency was analyzed with the building’s

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architectural, physical and occupational characteristics for better precision. Second one,

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temperature values for indoor temperature was set within a certain range related to changes in

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outside temperature, instead of using fixed seasonal temperature value. The aim was to

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provide comfort conditions in the building with the dynamic set-points and get more accurate

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results.

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A methodology was developed to integrate these diverse building systems with their

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operational arrangements through an automation system. Also, the developed method

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provides a sequence of procedures for stipulating simulation of the dynamic setpoints.

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2. METHODOLOGY

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A methodology is developed to integrate the divers building systems with their working

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schemes into the building’s features. It has been divided into five main phases. The first one is

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the identification of the building’s specifications in detail to model the building with its real

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conditions, which TRNSYS software is utilized. The model provides the energy performance

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of the system within the building’s architectural, physical and occupational characteristics.

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Afterward, phases are continued with the definition of three different control scenarios which

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are arranged as; definition and performance analyses of the base case scenario;

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description of the ICT and fuzzy scenarios. The control scenarios are investigated with the

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use of different software. The building energy model is prepared with TRNSYS where the

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scenario conditions were written on MATLAB and imported into the TRNSYS via a specific

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component which is used to connect MATLAB and TRNSYS. The last phase is the

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comparison and evaluation of the dynamic simulation results of these scenarios. The

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sequences of these steps are represented in Figure 1.

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1. Building Specifications

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• Local conditions of the building • Physical properties and sytems of the building 279 TRNSYS 280 281 282 283

284 285 286 287 288 289 290 291 292 293 294 295

2. Base Case Scenario • Identification of the inputs • Scenario working scheme • Analyses of the scenario performance 3. ICT Scenario • Identification of the new inputs • New scenario working scheme • Analyses of the scenario performance

296 297 298 299 300 301 302

• Energy performance modelling the building

4. Fuzzy Scenario • Identification of the new inputs • Fuzzy logic working scheme

Microsoft

Excel

• Analysis of the scenario performance

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5. Comparative Results • Dynamic simulation results • Graphical displays of the results • Comparison and evaluation of the results

Figure 1: Methodology

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2.1.

Identification of the Building Specifications

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This phase could be evaluated into three steps which are identifying the local conditions,

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defining the building features, and modeling the building.

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Firstly, the local conditions of the selected building are identified. Location and orientation of

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the selected building could have various conditions which have to be defined carefully.

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Moreover, whether the building is in the northern hemisphere or the southern hemisphere, it is

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near the sea or is far away, that are intensely related to the one of the important factors,

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weather data. This factor has a high degree of importance because it contains information

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such as the maximum and minimum temperatures, the heating and cooling degree days of the

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location that strongly affect the building heating and cooling demand

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After the definition of the local conditions, as the second step, the physical properties and the

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mechanical systems of the building, which should be carefully specified. The building area,

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building type, number of floors, applied materials, and their u-values, occupancy rate,

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infiltration rate, used equipment in the building could be considered as basic properties of the

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physical characteristics. Furthermore, the specification of mechanical systems with their

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components for the heating and cooling operations should be described. These components

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could be boilers, heat pumps, solar thermals, furnaces, pumps, fans that specifications such as

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capacity and quantifications should be determined.

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All steps up to this process are for preparing the model in support of the next procedure in

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order to represent the building correctly. Therefore, all the information should be collected

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carefully as they will be used as an input for the building model. There are several programs

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about establishing and simulating the building model to evaluate their energy performance,

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such as e-Quest, Design Builder, TRNSYS (e-Quest, 2019; Design Builder, 2019; TRNSYS,

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2019). Along with articles in the literature, there are theses, which were focused on comparing

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these simulation programs according to their functionality, accuracy, flexibility, clarity,

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usability, integration, adaptability and support (Coakley, 2014; Maile, 2010) In our study,

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TRNSYS is utilized.

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2.2.

Setting the Base Case Scenario

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The second phase is the description of the base case scenario which includes the building

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working scheme and consists of three steps; identification of the inputs, definition of the

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systems’ working scheme, and analyses of the system performance.

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As the first step, input variables for the base case-control scenario should be selected, and

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after the selection process, the methods of how to obtain the required data for these inputs

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should be determined and explained.

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Secondly, the definition of the base case-control system’s working scheme should be

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specified. Inputs of the scenario are explained in the previous step; now, the variables affected

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by the inputs should be clarified. These variables could have called as outputs of the system

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and control the related equipment of the mechanical systems to operate the on/off scenarios.

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These scenarios could be created by utilizing MATLAB and easily integrated into the

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TRNSYS by using a specific component in the program called Type155, which is used to

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connect MATLAB and TRNSYS. The critical point in this process is to modify the Type155

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appropriately with the MATLAB code. After writing the required codes for the base case-

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scenario in the MATLAB, which is the .m file, it must be imported in the component. The

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357

number of inputs and outputs must also be specified and linked with the related parameters in

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the TRNSYS model. As a consequence, it can be said that this step is the most important one

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because it explains how the whole systems were operated and controlled.

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The final step of this phase is to run the created model to obtain the system performance data.

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Nevertheless, this process also encountered various complications when the model is

362

simulated. The cause of the problems can be related to the incompatibility of programs,

363

meaningless simulation results, or design inaccuracies. When these problems occur, the entire

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process must be re-performed until being confident that the program works correctly and

365

results are sensible.

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2.3.

Setting the ICT Scenario

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This part of the methodology follows a similar path to the previous step with the additional

368

information. The ICT scenarios are usually more complicated than the base case-scenarios.

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The reason for this complexity is to improve the energy performance of the building by

370

controlling the building systems in more details with more inputs and outputs to achieve

371

reliable results. Hence, in addition to the previous step, there are some extra works to include

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in this stage, such as identifying the new inputs-outputs and provide an explanation of how to

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integrate them into the system.

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It should be noted that these changed conditions must be updated in the MATLAB codes and

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Type155 in order to run the program correctly. The integration of MATLAB into the

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TRNSYS could be seen in Figure 2. After all updates, the model must be run to see that the

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program

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378

output 1

input 1 input 2 .m file

output 2

output n

input n TRNSYS

related component with input 1

related component controlled by output 1

related component with input 2

related component controlled by output 2 Type155

related component with input n

related component controlled by output n

379

Figure 2: Integration of MATLAB into the TRNSYS

380 381 382

works or not, if it does not work, applied updates should be checked and adjusted until the

383

program works, and then, the reasonability of the results must be examined. If the simulation

384

outputs are realistic, the obtained data could be assumed as the performance of the ICT

385

system.

386

2.4.

Setting the Fuzzy Scenario

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The last scenario for this research is the implementation of fuzzy logic. The primary purpose

388

is to perform a dynamic simulation according to the working principle of the Fuzzy.

389

As in the previous sections, the input variables should be selected and explained. After the

390

selection of inputs, classification should be made, such as good, bad, average based on their

391

values. However, there is no specified value for the good, bad, or average ranges. Some points

392

have uncertainties due to human feeling, for instance, considering different comfort

393

conditions depending on genders and ages. Therefore, each determined group should have

394

intersections between them while defining. 15

395

The next step is to define the fuzzy logic system working principals. Two main steps should

396

be taken to complete the fuzzy logic working scenario. These are the explanation of outputs

397

and the determination of rules. The outputs are the variables that affected by the inputs

398

according to the identified rules by the user. There would be more than one output depending

399

on the user’s request. The identification of the rules is the critical point in the fuzzy logic

400

system because the whole system works based on these rules. These rules can be easily

401

specified by the user thanks to the user-friendly interface.

402

After all these processes, the fuzzy logic system is ready to import into the TRNSYS. Finally,

403

the simulation could be run to obtain the results.

2.5.

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Comparative Results

405

The final step of the methodology is the comparison and evaluation of the obtained results to

406

realize the improvements in the energy performance of the building, caused by the ICT

407

systems and fuzzy logics. All of the stages mentioned above should be considered for getting

408

accurate results. After receiving the results from all of the scenarios, the savings could be

409

calculated. The results could be taken by 1-hour intervals with the help of TRNSYS.

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3. CASE STUDY: KARTAL ELDERLY HOUSE

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3.1.

Building Specification

413

The building is located in Kartal, Istanbul, Turkey. Kartal is located on the Asian side and the

414

southeast part of the Istanbul, on the coast of the Marmara Sea as it is represented in Figure 3.

415 416 417

A B

C

418 419

Figure 3: A. Location, B. Aerial view, C. Image of the building 16

420

The building was designed as an elderly building and completed in 2005. Hence, between

421

2012 to 2018, it was restored with the aim of energy efficiency. The region has the temperate

422

climate; in summers, the weather is warm and humid with some rain. On the other hand, in

423

winters, it could be cold and wet with little snow. Rest of the year can be called moderate. In

424

2017, the HDD and CDD were 1662 and 255, respectively, according to the Turkish State

425

Meteorological Service (Turkish State Meteorological Service, 2018). The number of days

426

that the mean temperature of the day is equal or below 15˚C were 183, which means heating

427

demand. Thus, the number of days that the mean temperature of the day was higher than 22˚C

428

is 90, which means cooling demand as it is represented in Table 1. Whence, it can be easily

429

said that the heating demand will be higher than the cooling demand that heating problems

430

must be carefully acknowledged.

431

Table 1: HDD and CDD of Istanbul Istanbul 1662 183 255 90

HDD T ≤ 15˚C CDD T > 22˚C 432 433

The building occupied by elderly people has a total conditioned floor area of nearly 18.108

434

m2, distributed over 8 floors; 2 of them are underground, and the rest are above ground. The

435

building’s aerial and front views are shown in Figure 3.

436

The foremost physical characteristics of the building are summarized as, the U-values of the

437

external walls, below-grade walls, flat roof, ground floor, and windows are 0.330, 0.950,

438

0.620, 0.482 and 1.6 W/m2-K respectively. Window to wall ratio is nearly 30%. The other

439

features such as occupancy rate, infiltration rate, the power density of the office equipment

440

and normalized power density of lighting have 0.07 people/m2, 1.1 ac/h, 6.0 W/m2 and 2.35

441

W/m2-100lux values respectively. These properties are represented in Table 2.

442 17

Table 2: Physical properties of the building

443 Property

Value

Property

Value

External wall U-value

0.330 W/m2K

Occupancy rate

0.07 people/m2

Below grade wall U-value

0.950 W/m2K

1.1 ac/h

Flat roof U-value

0.620 W/m2K

Infiltration rate Power density of office equip.

Ground floor U-value

0.482 W/m2K

Power density of lighting

Windows U-value

1.600 W/m2K

Windows to wall ratio

6.0 W/m2 2.35 W/m2100lux 30%

444 445

The most important aspect of this step was to define the mechanical systems accurately

446

because the main purpose of this study was to evaluate the heating and cooling demands. 3

447

boilers, 3 water source heat pumps (WSHP) and 4 air source heat pumps (ASHP) were used in

448

the mechanical system of the Kartal building. Boilers were used for only heating, while air

449

source and water source heat pumps were used both for heating and cooling. The capacity of

450

each boiler, WSHP and ASHP were 100 kW, 200 kW, and 130 kW respectively. Also, 150

451

solar panels were used to produce hot water.

452

The modeling step were started with this inputs. The building model must include all the

453

physical characteristics with well-defined mechanical systems. Modeling the building should

454

be as basic as possible to simplify the simulation that is the whole building divided into three

455

zones called A-block, B-block, and atrium. A-block and B-block had the same volume as

456

25,125 m3, while atrium had 5,625 m3 as represented in Figure 4.

457 458

Figure 4: Zones of the building

18

459

3.2.

Base Case Scenario

460

The mechanical system of the building works according to their communication competence

461

with each other. One system might be controlled by output of another system. For this

462

scenario, only one controlling input was determined which was the outlet temperature of the

463

solar collectors. The system’s working scheme was adjusted according to the situation result

464

of the outlet temperature. The mechanical systems of the building are represented in Table 3. Table 3: Mechanical systems

465

Mechanical System Boiler Air Source Heat Pump Water Source Heat Pump Solar Collectors

Number 3 4 3 150

Purpose Heating Heating and Cooling Heating and Cooling Water Heating

466 467

The base case scenario has a very basic control scenario and was divided into 2 seasonal

468

modes as winter mode and summer mode to control the mechanical system under different

469

conditions. The mechanical equipments affected by the inputs were; ASHP, WSHP, and

470

boiler for both winter and summer modes. In winter mode, there were two conditions as

471

Tcollector≥45˚C and Tcollector<45˚C, while there was no condition in the summer mode. While

472

ASHP and WSHP always work, boiler becomes on or off situation according to the status of

473

the inputs. Table 4 shows the conditions of the base case scenario.

474

Table 4: Base case scenario working scheme

475

Seasonal Mode Winter Mode Summer Mode

Input

ASHP

WSHP

Boiler

Tcollector≥45˚C Tcollector<45˚C -

On On On

On On On

Off On Off

476

19

477

After these conditions were defined on the MATLAB, the .m file was imported into Type155

478

component in the model which was created in advance. It consists of 1 input variable, which

479

is an outlet water temperature of the solar collectors (Tcollector) and 3 on/off output control

480

variables as Air Source Heat Pump, Water Source Heat Pump, Boiler. The process of

481

importing the .m file into the TRNSYS should be taken careful consideration since it could be

482

encountered several problems such as the difficulty of creating a proper integration between

483

MATLAB and TRSYS as well as the suitability of the format of the code.

484

3.2.1. Analysis of the Base Case Scenario

485

The results provided from the first scenario are represented in Table 5, which are delivered for

486

per month of individual heating, cooling, and total energy consumption. Table 5: Base Case Scenario Results

487

January February March April May June July August September October November December

Heating Consumption [kWh] 303,302.70 249,616.19 221,334.72 154,243.38 77,368.80 0.00 0.00 0.00 0.00 105,454.31 204,229.37 280,873.49

Cooling Consumption [kWh] 0.00 0.00 0.00 0.00 24,327.35 64,894.98 124,370.08 115,054.80 49,650.32 12,215.09 0.00 0.00

Total Consumption [kWh] 303,302.70 249,616.19 221,334.72 154,243.38 101,696.15 64,894.98 124,370.08 115,054.80 49,650.32 117,669.40 204,229.37 280,873.49

Total Total [kWh/m2]

1,596,422.97 88.16

390,512.63 21.57

1,986,935.60 109.73

488

20

489

3.3.

ICT Scenario

490

The inputs were increased in this scenario from one to two. The first one was the same as the

491

previous scenario, and the additional one was the outside temperature. The historical weather

492

data were included in the simulations. This scenario was also divided into 2 parts as it was

493

before. However, the process was more complicated than the base case because there were

494

two inputs to observe more working conditions.Yet, the affected equipment was remain the

495

same. In summer mode, there were two conditions as Tcollector≥18˚C and Tcollector<18˚C, while

496

several conditions exist in the winter mode. The working scheme and the conditions were

497

represented in the Figure 5 and Table 6. In the last step, the previously written codes were

498

updated to attain the results of the ICT scenario. Also, in Type155 component, the number of

499

inputs was increased to 2, while there were no changes in the number of outputs. After these

500

changes, the model was rerun, and the results were obtained.

501 502

Figure 5: Working scheme of the ICT scenario

503

Table 6: ICT scenario working scheme Seasonal Mode Winter Mode

Input 1

Input 2

ASHP

WSHP

Boiler

Tcollector≥45˚C 40˚C
Tout<12˚C Tout<12˚C Tout<12˚C

off off on

on on on

off on on

21

Summer Mode

Tcollector≥45˚C Tcollector<45˚C Tcollector≥18˚C Tcollector<18˚C

Tout≥12˚C Tout≥12˚C -

on on on off

off on on on

off on off off

504 505

3.3.1. Analysis of ICT Scenario

506

The obtained results from the ICT scenario represented in Table 7. As in the previous

507

scenario, the results were shown per month for heating, cooling, and total energy

508

consumption. Table 7: ICT Scenario Results

509

January February March April May June July August September October November December Total Total [kWh/m2]

Heating Consumption [kWh] 289,531.5 237,292.17 207,787.07 141,950.94 68,191.45 0.00 0.00 0.00 0.00 94,566.60 191,403.48 266,931.92 1,497,655.14 82.71

Cooling Consumption [kWh] 0.00 0.00 0.00 0.00 24,619.04 61,793.51 115,051.30 106,119.52 46,743.49 12,537.09 0.00 0.00 366,863.95 20.26

Total Consumption [kWh] 289,531.51 237,292.17 207,787.07 141,950.94 92,810.49 61,793.51 115,051.30 106,119.52 46,743.49 107,103.69 191,403.48 266,931.92 1,864,519.09 102.97

510 511

3.4.

Fuzzy Scenario

512

With the Fuzzy application, a dynamic hourly simulation was performed by changing the

513

setpoint values within a specific range instead of using fixed seasonal setpoints. The aim was

514

to provide comfort conditions in the building with the dynamic setpoints and get more

515

accurate results. As in the previous scenarios, firstly, the input variables were selected. These

516

were set as external temperature (Tout), and indoor temperature (Tin) which are set as inputs

22

517

and setpoint is defined as the output as represented in Figure 6. Each input variable had 5 sub-

518

groups as very cold, cold, comfort, hot and very hot. The very cold, cold, comfort, hot and

519

very hot ranges of indoor temperatures were below 15°C, 12-20°C, 18-27°C, 25-32°C and

520

upper than 29°C respectively. For the outdoor temperature, the ranges were very cold below

521

8°C, cold between 5-21°C, comfort 17-27°C, hot 25-33°C and very hot upper than 30°C as

522

represented in Table 8 below.

523

Likewise, intersections were created between these groups while specifying them.

524

Nonetheless, these intersections generate some uncertainty at the time of deciding where the

525

input values were belonged. These uncertainties constitute the working principle of Fuzzy

526

logic.

527 528

Figure 6: Tindoor(left) and Toutdoor(right) fuzzyfication

529 530

The next step was to define the output variables where were chosen based on asseted point-

531

temperatures in our study,. Two different setpoint ranges were assigned, one for the heating

532

season as 22-25°C and another for the cooling season as 23-26°C as represented in Figure 7.

533

The reason for identifying two different ranges, the setpoints for heating and cooling seasons

534

were different in the base case and ICT scenarios.

535

23

536 537

Figure 7: Fuzzyfication of the heating (left) and cooling (right) setpoints

538 539

There was only one issue left to ensure the working logic between inputs and output in the

540

fuzzy logic scenario; assignment of the rules. The main structure of the rules was followed the

541

following path; 'if input 1 is x and/or input 2 is y, then output is z.'. Some of the rules (totally

542

25 rules) defined in this study were represented in Figure 8.

543 544

Figure 8: Defined rules

545

Correspondingly, the overview of the fuzzy scenario could be seen in Table 8. Further, the

546

diagram of the fuzzy logic system which explains the relations between inputs and output is

547

shown in Figure 9. The vertical axis represents the setpoints while the horizontal axis

548

indicates the indoor and outdoor temperature.

549

Table 8: The overview of the fuzzy scenario

550

Input

Very Cold Cold

Indoor Temperature Below 15°C 12-20°C

Rules Outdoor Temperature Below 8°C 5-21°C 24

25 rules

Output SetpointTemperature -

Comfort Hot Very Hot Heating Mode Cooling Mode

18-27°C 25-32°C Upper than 29°C -

17-27°C 25-33°C Upper than 30°C -

22-25°C 23-26°C

551 552

Figure 9: Diagram of the fuzzy logic

553

Figure 11 represents the generated logic case. The blue regions represent the low setpoints

554

according to the inside and outside temperatures situation, while the yellow regions indicate

555

the high set points. The greenish colors display the transition setpoints that were decided by

556

the working principle of the Fuzzy logic. This part was the most significant because there

557

were no particular values like the high and low setpoints; the values could be changed in that

558

range.

559

3.4.1. Analysis of Fuzzy Scenario

560

3.4.1.1.

Hourly based results

561

In this scenario, the main objective was to see the dynamic setpoint changes according to

562

outdoor and indoor temperatures. Therefore, two days, one of the hottest for the heating

563

season and one of the coldest for cooling season, were selected to track the hourly changes of

564

the setpoints as it represented in Figure 10 and Figure 11.

25

565

566

Figure 4: Hourly setpoint changes in a day for the heating season

567 568

Figure 11: Hourly setpoint changes in a day for the cooling season

569 570

It is clearly seen from the graphs that the setpoints varies according to the Tin and Tout. In the

571

heating day, the setpoint reached the highest degree with 24.62 °C when the Tout was 4.50 °C.

572

The minimum setpoint value was 22.38 °C. The range of the setpoints was between 22.38 °C

573

and 24.62 °C for that day, and it could be any value in that range.

26

574

3.4.1.2.

Monthly based results

575

The results were obtained from the last scenario and represented in Table 9. As in the

576

previous scenarios, the results were shown per month as heating, cooling, and total energy

577

consumption. Table 9: Fuzzy Scenario’s Effect on Energy Consumption

578

January February March April May June July August September October November December

Heating Consumption [kWh] 290,537.24 233,942.50 198,446.90 128,533.29 56,811.74 0.00 0.00 0.00 0.00 81,097.90 180,987.15 260,576.99

Cooling Consumption [kWh] 0.00 0.00 0.00 0.00 21,575.56 55,966.86 105,795.16 96,875.45 41,379.09 10,823.05 0.00 0.00

Total Consumption [kWh] 290,537.24 233,942.50 198,446.90 128,533.29 78,387.30 55,966.86 105,795.16 96,875.45 41,379.09 91,920.95 180,987.15 260,576.99

Total Total [kWh/m2]

1,430,933.71 79.02

332,415.17 18.36

1,763,348.89 97,38

579 580

3.5.

Comparative Results

581

Several simulations were performed to obtain the results. For this purpose, firstly a baseline

582

model was created. Later, different codes were written on the MATLAB to run the

583

simulations according to conditions of scenarios. Then, these .m files were imported into the

584

TRNSYS model, and simulations were run.

585

Consequently, the results were obtained to compare the scenarios. The comparisons were

586

made considering the annual heating and cooling energy consumption. The monthly

587

cumulative heating and cooling energy consumptions of the scenarios were compared and

27

represented in Figure 12 and 13. In both figures, the blue, red, and green line express the base

589

case, ICT, and fuzzy scenarios, respectively.

ENERGY CONSUMPTION (KWh)

588

Heating season

Heating season

TIME

BC-heating

ICT-heating

Fuzzy-heating

590 591 592

Figure 12: Annual comparison of heating energy consumptions of the scenarios

593

As represented in Figure 12, the curves were increased during the heating seasons and

594

remained constant when no heating was required.. The most savings were earned from the

595

fuzzy logic. As a result, the implementation of the ICT and fuzzy logic on the simulation

596

model were resulted a considerable amount of reduction on heating energy consumption

597

during heating months. The total heating energy consumption was 1,596,422.97 kWh in the

598

base case, 1,497,655.14 kWh in the ICT and 1,430,933.71 kWh in the fuzzy logic. It shows

599

that the annual saving was % 6.19 for ICT and %10.37 for fuzzy logic. Furthermore, the

600

heating consumption of per square meter was improved from 88.16 kWh/m2 to 82.71 kWh/m2

601

and 79.02 kWh/m2. Energy consumptions for each month is represented in Table 10.

602

Table 10: Comparison of heating energy consumption. Heating Energy Consumption Saving Saving Base Case ICT Fuzzy ICTFuzzy[kWh] [kWh] [kWh] BC (%) BC (%)

28

Saving FuzzyICT (%)

January February March April May June July August September October November December Total Total [kWh/m2]

303,302.70 249,616.19 221,334.72 154,243.38 77,368.80 0.00 0.00 0.00 0.00 105,454.31 204,229.37 280,873.49

289,531.51 237,292.17 207,787.07 141,950.94 68,191.45 0,00 0,00 0,00 0,00 94,566.60 191,403.48 266,931.92

290,537.24 233,942.50 198,446.90 128,533.29 56,811.74 0.00 0.00 0.00 0.00 81,097.90 180,097.15 260,576.99

1,596,422.97 1,497,655.14 1,430,933.71 88.16

82.71

79.02

4.54 4.94 6.12 7.97 11.86 0.00 0.00 0.00 0.00 10.32 6.28 4.96

4.21 6.28 10.34 16.67 26.57 0.00 0.00 0.00 0.00 23.10 11.38 7.23

-0.35 1.41 4.50 9.45 16.69 0.00 0.00 0.00 0.00 14.24 5.91 2.38

6.19

10.37

4.46

6.19

10.37

4.46

ENERGY CONSUMPTION (KWh)

603

Cooling season

BC-heating

ICT-heating

Fuzzy-heating

604 605

Figure 5: Annual comparison of cooling energy consumptions of the scenarios

606

The cooling consumption alternatively is represented in Figure 13. The ranges were started to

607

broaden from June to September which are the cooling season.. Nonetheless, during the

608

transition months such as April, a small increase was seen. The total cooling energy

609

consumption of the base case, ICT scenario, and Fuzzy scenario were 390,512.63 kWh,

610

366,863.95 kWh, and 332,415.17 kWh respectively. The saving is about 23,648.68 kWh for

611

ICT and 58,097.46 kWh for Fuzzy for one year according to simulations. Besides, the cooling 29

612

consumption of per square meter was improved from 21.57 kWh/m2 to 20.26 kWh/m2 and

613

18.36 kWh/m2. The total savings for cooling energy consumption was 6.06% and 14.88% for

614

ICT and Fuzzy. Table 11 summarizes cooling energy consumptions. Table 11: Comparison of cooling energy consumption.

615

January February March April May June July August September October November December

Cooling Energy Consumption Saving Saving Base Case ICT Fuzzy ICT-BC Fuzzy-BC [kWh] [kWh] [kWh] (%) (%) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 24,327.35 24,619.04 21,575.56 -1.20 11.31 64,894.98 61,793.51 55,966.86 4.78 13.76 124,370.08 115,051.30 105,795.16 7.49 14.94 115,054.80 106,119.52 96,875.45 7.77 15.80 49,650.32 46,743.49 41,379.09 5.85 16.66 12,215.09 12,537.09 10,823.05 -2.64 11.40 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Total 390,512.63 366,863.95 332,415.17 2 Total[kWh/m ] 21.57 20.26 18.36

6.06 6.06

14.88 14.88

Saving Fuzzy-ICT (%) 0.00 0.00 0.00 0.00 12.36 9.43 8.05 8.71 11.48 13.67 0.00 0.00 9.39 9.39

616 617

4. CONCLUSION and DISCUSSION

618

In this paper, the energy performance analyses of a large-scale building’s mechanical system

619

with different automation conditions were performed. The aim is to identify and improve the

620

effect of the building automation systems on the heating and cooling energy consumption by

621

controlling the building systems’ working scheme. It is widely recognized that working

622

scheme of building systems often brings additional energy consumption that is not overseen

623

during the design that should be included for the building operation process.

624

Therefore the paper demonstrates a method to evaluate the energy performance of the

625

propose system with its automation. It further provides a sequence of procedures for 30

626

stipulating simulation of dynamic set-points to get more accurate results. A divers system as

627

heat pumps with its connection to boilers, and solar collectors was designed with the

628

consideration of all building’s specifications. Therefore, whole system efficiency was

629

analyzed with the building’s architectural, physical and occupational characteristics for better

630

accuracy. Moreover, with the Fuzzy application, a dynamic hourly simulation method is

631

proposed which performed by changing the set-point temperature values within a certain

632

range instead of using fixed seasonal set-points. The objective is to improve the building

633

energy efficiency while providing comfort conditions in the building and get more accurate

634

results.

635

TRNSYS and MATLAB software were utilized for performing the analyses, The TRNSYS

636

software gives the opportunity to create the building model with its individual mechanical

637

system in detail and allows controlling these systems straightforward. Consequently, the

638

simulations give more accurate results. The other characteristic is the easy integration and

639

connection capability with the other software like MATLAB, provides the effectiveness of

640

implicating the control strategies as represented in this study.

641

Consequently, the results of this study showed the effect of automation systems on the

642

building' energy performance. The savings were calculated for both heating and cooling

643

consumption separately. The austerities come from the ICT were 6.19% for heating and

644

6.06% for cooling. Finally, the key purpose of this study was to perform a dynamic hourly

645

simulation, and it was achieved by the implementation of the Fuzzy logic. The hourly setpoint

646

changes were shown in two different seasonal days. It was seen that the setpoint dynamically

647

varied in the interval of 2-3 °C. Besides, the savings were obtained with only changes on the

648

setpoints. According to the results, the saving on heating consumption between Fuzzy and

649

Base Case scenarios was about 10.37% (~166,000 kWh), while it was 4.46% (~65,000 kWh)

650

for Fuzzy and ICT scenarios. The saving for the cooling consumption was nearly 14.88% 31

651

(58,000 kWh) for Fuzzy and Base Case scenario, while it was 9.39% (35,000 kWh) for Fuzzy

652

and ICT scenarios as illustrated in Table 12. The savings between Fuzzy and ICT were about

653

100,000 kWh for one year, which considerably high. These results indicate that the dynamic

654

setpoints improve the energy-saving, which could be simulated accurately.

655 656

Table 12: Summary of total heating and cooling savings

Base Case ICT Fuzzy ICT- Fuzzy

Heating Saving 6.19 % 4.46 % 10.37 %

Cooling Saving 6.06 % 9.39 % 14.88 %

657 658

On the other hand, overall savings of ICT and Fuzzy application is considerably high which

659

was reached 10.37% (~166,000 kWh) for heating and 14.88% (58,000 kWh) for cooling with

660

total about 224,000 kWh for one year. Inclusively, remarkable reduction on the energy

661

consumption of the buildings could be accomplished with the application of the automation

662

systems especially large-scale buildings because of their high degree of energy consumption

663

and multiple mechanical systems usage.

664

The paper was established through a case study; however, the method could be applicable for

665

any building or location.

666 667 668 669 670 671 672 673 674 675

ACKNOWLEDGMENT:

676 677 678

Buker M., Riffat S. 2016. Solar assisted heat pump systems for low temperature water heating application: A systematic review. Renewable and Sustainable Energy Reviews 2016; 55: 399. https://doi.org/10.1016/j.rser.2015.10.157

This research has been supported by a European Union project called “Residential Renovation towards nearly zero energy CITIES” (R2CITIES) Grant Agreement No 314473 (R2CITIES, 2018).

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Highlights:

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the paper identifies and improve the effect of the building automation systems

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diverse building systems with their operational arrangements were controlled and simulated

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fixed seasonal set-point temperature values were changed within a certain range

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It provides a sequence of procedures for stipulating simulation of dynamic set-points

We wish to draw the attention of the Editor to the following facts which may be considered as potential conflicts of interest. The project that was used as a case study in the research has been supported by European Union under the FP7, the project called “Residential Renovation towards nearly zero energy CITIES” We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the order of authors listed in the manuscript has been approved by all of us. The corresponding author is the sole contact for the editorial process. She is responsible for communicating with the other authors about progress, submissions of revisions and final approval of proofs. We confirm that we have provided a current, correct email address which is accessible by the Corresponding Author and which has been configured to accept email from [email protected]. Hatice Sözer Fatih Tüysüz