An integrated system for buildings’ energy-efficient automation: Application in the tertiary sector

Applied Energy 101 (2013) 6–14

Contents lists available at SciVerse ScienceDirect

Applied Energy journal homepage: www.elsevier.com/locate/apenergy

An integrated system for buildings’ energy-efficient automation: Application in the tertiary sector Vangelis Marinakis ⇑, Haris Doukas, Charikleia Karakosta, John Psarras National Technical University of Athens, School of Electrical & Computer Engineering, Management & Decision Support Systems Laboratory (EPU-NTUA), 9, Iroon Polytechniou Str., 157 80 Athens, Greece

h i g h l i g h t s " We developed an interactive software for building automation systems. " Monitoring of energy consumption in real time. " Optimization of energy consumption implementing appropriate control scenarios. " Pilot appraisal on remote control of active systems in the tertiary sector building. " Significant decrease in energy and operating cost of A/C system.

a r t i c l e

i n f o

Article history: Received 6 December 2011 Received in revised form 27 April 2012 Accepted 25 May 2012 Available online 22 June 2012 Keywords: Energy efficiency Green building Automation systems Building energy management systems

a b s t r a c t Although integrated building automation systems have become increasingly popular, an integrated system which includes remote control technology to enable real-time monitoring of the energy consumption by energy end-users, as well as optimization functions is required. To respond to this common interest, the main aim of the paper is to present an integrated system for buildings’ energy-efficient automation. The proposed system is based on a prototype software tool for the simulation and optimization of energy consumption in the building sector, enhancing the interactivity of building automation systems. The system can incorporate energy-efficient automation functions for heating, cooling and/or lighting based on recent guidance and decisions of the National Law, energy efficiency requirements of EN 15232 and ISO 50001 Energy Management Standard among others. The presented system was applied to a supermarket building in Greece and focused on the remote control of active systems. Ó 2012 Elsevier Ltd. All rights reserved.

1. Introduction The current financial and economic crisis as well as the wider environmental pressures, including climate change and security of energy supply put energy back at the ‘‘heart’’ of the European Union’s (EU) action. In this context, ambitious targets have been set for 2020 (‘‘20–20–20’’ energy policy package) aiming to foster European economy to more sustainable energy paths [1]. This policy is a first resolute step towards achievement of the low-carbon economy ultimate goal, whilst making at the same time the consumed energy more secure, competitive and sustainable. Nowadays, buildings are responsible for about 40% of the EU’s total final energy consumption and greenhouses gas (GHG) emissions, putting them among the largest end-use sectors globally [2]. However, taking into consideration their untapped potential for cost-effective energy savings (estimated at 1509 Mtoe by ⇑ Corresponding author. Tel.: +30 210 7723514; fax: +30 210 7723550. E-mail address: [email protected] (V. Marinakis). 0306-2619/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.apenergy.2012.05.032

2050) the penetration of energy efficiency technologies in the building sector could play an active role among the EU’s efforts in development of a viable strategic framework towards a lowcarbon economy [3]. In October 2006, the European Commission (EC) adopted an Action Plan for Energy Efficiency (2007–2012) following the Green Paper for Energy Efficiency aiming at 20% reduction on energy consumption by 2020 [4]. A comprehensive framework of directives supports this initiative, key of which are the Directive 2006/32/ EC on energy end-use efficiency and energy services and the Directive 2002/91/EC on energy performance of buildings [5,6]. Furthermore, the Directive 2003/66/EC on labelling of refrigerators [7], the Directive 2002/40/EC on labelling of electric ovens [8], the Directive 2002/31/EC on labelling of air-conditioners [9], the Directive 2000/55/EC on energy efficiency requirements for ballasts for fluorescent lighting [10], as well as the Regulation 2422/2001/EC on Energy Star labelling for office equipment [11] foster an integrated European legislative framework for the promotion of energy efficiency and green buildings. Furthermore, the recent Directive

V. Marinakis et al. / Applied Energy 101 (2013) 6–14

2010/31/EU promotes the improvement of buildings’ energy performance within the whole EU, with the ultimate goal of ensure all new buildings are net zero energy consumers by 2020, ‘‘Nearly zero-energy building’’ [12]. As a result of the Directive of Energy Performance of Building (EPBD) a number of EN standards have been developed to harmonize the energy calculation methods concerning buildings. In this context, a new European standard EN 15232 ‘‘Energy Performance of Buildings – Impact of Building Automation, Control and building Management’’ was compiled to support the EPBD [13]. The standard describes methods for evaluating the influence of building automation and technical building management on the energy consumption of buildings. Four efficiency classes A to D have been introduced to this purpose. Furthermore, the ISO 50001 ‘‘Energy Management Standard’’ enables organizations to establish the systems and processes necessary to improve energy performance [14]. More especially, the ISO 50001 aims to establish a framework for industrial plants, commercial, institutional and governmental facilities to increase energy efficiency, reduce costs and improve energy performance. The successful fulfillment of the abovementioned can be supported by developing actively advance new technology applications and especially technologies for energy efficiency and renewable or zero carbon buildings [15], as well as by marketbased tools (mainly taxes, subsidies and the CO2 emissions trading scheme) and by community financial instruments [16]. In this context, Sgouridis and Kennedy (2010) presented an integrated system for total energy management and accounting on a city-wide scale [17]. A scientific reference system has been also set up to enhance availability, quality and completeness of data on new energy technologies, energy end-use efficiency, as well as measures to support the related energy research and technology development [18]. In recent years, great efforts have been focused on green building constructions revealing the ongoing interest of the scientific community on this topic [19]. A number of methodologies and tools have been developed regarding the energy performance and energy efficiency measures in the building sector [20–22]. In particular, the impact of energy efficiency measures on the economic value of buildings has been studied by Popescu et al. [23]. Moreover, the impact of climate change on building energy use has been thoroughly examined by Wan et al. [24]. A number of studies exist, which mainly focus on the simulation [25,26] and optimization [27] of energy consumption in the building sector. In this context, integrated building automation systems are applied to control heating, ventilation, and air-conditioning (HVAC) systems, lighting, pumps and lifts [28–32]. To the best of our knowledge, techniques for HVAC control, such as pole-placement, optimal regulator and adaptive control have been presented [33,34]. A number of studies have been also implemented regarding state of the art control systems in buildings [35–38], as well as HVAC simulation in building energy management [38,39,35,40]. In the past, less ‘‘intelligent’’ systems were used for energy and environmental management, mainly for optional monitoring of energy consumption with insufficient control functions and high dependence on the human factor [41,42]. The majority of recent developments have followed the advances made in computer technology, telecommunications and information technology [43–47]. In addition, the building electrical facilities are nowadays in a stage of transition. Indeed, the building’s facilities are becoming more and more complex, and the needs for interaction among them increases [48]. Although building automation systems have developed and have become increasingly popular, the necessity for intelligent tools and methods to provide remote control and real time monitoring of energy consumption, remains [49,50]. The current empirical and simulation models, such as SBCI 2010 [51], EnergyPlus

7

2009 [52], eQuest 2009 [53], provide very little flexibility, especially when accounting for occupants, ignoring the impact of their behavior on building energy use [54]. To the best of our knowledge, a system which also includes remote control technology to enable energy end-users to monitor the energy consumption and control the operation of buildings’ appliances, as well as optimization functions for the reduction of the energy consumption is required. Such an integrated system is not present in the international scientific literature. This is particular true for Greece, which beyond the current economic crisis, faces numerous challenges associated with the high and volatile energy prices [55]. Indeed, the existing Greek building stock needs appropriate interventions to improve the poor quality of construction practiced until the 1990s [56]. Greece has recently incorporated the Directive 2002/91/EC on the energy performance of buildings, as well as the related procedures and modalities, so as to be fully harmonized with European directives and commitments [57]. To respond to this common interest, this paper aims to present an integrated system for the simulation and optimization of energy consumption in the building sector, providing a supportive tool for the energy end-users of industrial, domestic, tertiary and public buildings. The proposed system is based on a prototype software tool for the simulation and optimization of energy consumption in the building sector, enhancing the interactivity of building automation systems. The pilot appraisal is focused on the remote control of air conditioning (A/C) system during the summer peak hours, maintaining the desirable comfort. Apart from the introduction the paper is structured along four sections. The second section is devoted to the presentation of the interactive software for building energy and environmental management in terms of its philosophy and the procedure followed. The third section is devoted to the system’s pilot appraisal and results. Finally, in the last section the main points drawn up from this paper are summarized.

2. The proposed interactive software 2.1. Background Building automations are a fundamental part of the energy management systems. However, the traditional automation system may have limited level of intelligence, based on their purely ‘‘mechanical logic’’. In the traditional automation systems, each sensor and actuator needs its own wiring, which makes the initial installation cost high. Expansion is also a problem, and even ongoing maintenance costs are high. In this context, the hierarchy in industrial automation systems is presented in Fig. 1. Data exchange takes place both between and within the different levels. To this end, numerous systems and communication protocols have been developed at international level, mainly Dupline, European Installation Bus (EIB), BatiBus, European Home Systems Protocol (EHS), X-10 international standard for communication among electronic devices, Consumer Electronics Bus (CEBus), Home bus System (HBS) as well as C-Bus communications protocol for home and Building automation, among others [58–61]. The higher level in the pyramid needs to handle multiple systems within the different levels using lower number of components and higher amount of data. In particular:  Component level: Sensors, switches, relays, as well as valves, motors and other units comprise the lower level of the industrial automation pyramid.  Device level: Counters and timers store, display and control the sequence of an event or process.

8

V. Marinakis et al. / Applied Energy 101 (2013) 6–14

Energy & Environmental Management Simulation & Optimisation of Energy Consumption

Collection

Storage

Energy Data

Statistic Analysis

Control Scenarios

Command Control

Master Generator Meters

Fig. 1. Industrial automation pyramid.

 Process level: Programmable logic controllers (PLCs) are a process control system based on a set of digital and analog I/O received from the assigned sensors and actuators on the components level. Larger more complex systems can be controlled by a Supervisory Control and Data Acquisition (SCADA) system or Personal Computer (PC) logging all events and alarms, archiving all measured readings and graphically displaying the status of the operational systems.  Plant level: The last level of industrial automation pyramid focuses on high-level planning and control application in the plant operation that is be used for design, analysis, optimization, process planning, production scheduling, materials handling, inventory control, maintenance, and marketing. Computer-aided design (CAD), computer-aided engineering (CAE) and computer-aided manufacturing (CAM) systems are the most common examples. For the purpose of this paper the building automation system that was used is the Dupline system. The decentralized building automation system Dupline combines heat, lighting, A/C and other building monitoring procedures providing high standards of comfort, security and energy saving potential [48]. Compared to a traditional installation the wiring of a Dupline system is much simpler and the flexibility of change and expansion is significantly increased. Indeed, the automation system Dupline can transmit multiple digital and analog signals over long distances by the use of a standard 2-wire cable. Therefore, serial bus technology has become an increasingly important part of the concepts of electrical installations for buildings. All the other units of an installation, such as input–output (I/O) units, energy meters and sensors are connected through the pair of wires to Master Generator, which processes the signals coming from the various bus networks. Moreover, in bigger buildings installation many Dupline signal generators can simultaneously connect for data exchange.

I/O Modules

Energy End-Users

Interactive Software

Sensors

Building Automated Systems – Dupline Fig. 2. Interactive software’s philosophy.

energy efficiency and economic performance. Hence, the endusers identify the energy consuming sectors of the building through real time monitor and comparisons of energy consumption profiles from different time periods.  Optimization: At the same time, this management tool has the ability of running alternative optimization scenarios for achieving ‘‘intelligent’’ management of the building electric loads towards efficient energy and environmental management. 2.3. Procedure The available Dupline building automation drivers, namely Active X Server and Dynamic Data Exchange (DDE) Server used to develop specific applications, as depicted in Fig. 3. The software tool developed can be appropriately customized to the users’ requirement and building characteristics. On a first level, the proposed tool is applied, tested and optimized on the ‘‘Demo building Automation System’’, a simulation system of building automations incorporating the necessary equipment as illustrated in Fig. 4. Indeed, this system consists of Master Generator, I/O units (relays, dimmers, etc.), sensors (indoor temperature sensor, motion detector, light lux sensor), sockets and switches for load control, as well as an energy analyzer (EM 24DIN). During the tests smaller loads were used, such as lighting lamps to simulate the A/Cs’ operation. The followed procedure is shown in Fig. 5. More specifically the procedure is described as follows:

The proposed interactive software tool serves the development of a friendly and graphically-based user interface. The system’s general philosophy is presented in Fig. 2. Energy end-users have the capacity to monitor the energy consumption and control the operation of buildings’ appliances without their active involvement in the different parts of buildings’ installation. In particular, the proposed tool includes:

 Step 1 – Energy users’ profile: The user provides general information about the building and the consumed quantities of each energy form. Moreover, a portable electrical energy analyzer is used for determining building user’s behavioral profile. The energy consuming sectors of the building and also the untapped energy efficiency potentials, depending on the energy use profile of the building and the users’ requirement, can be identified.  Step 2 – Identification of energy-efficient automation functions: The system incorporates energy-efficient automation functions for heating, cooling, ventilation and/or lighting based on the: – Recent guidance and decisions of the National Law. – Guides of American Society of Heating, Refrigerating and AirConditioning Engineers (ASHRAE). – Energy efficiency requirements of EN 15232. – ISO 50001 Energy Management Standard.

 Simulation: This tool provides organized and statistically analyzed data sets on the energy use in the buildings and their

The proposed tool provides effective automation and control of heating/cooling, ventilation/air conditioning and lighting that

2.2. Philosophy

9

V. Marinakis et al. / Applied Energy 101 (2013) 6–14

Ι/Ο Units

Meters

Dupline Master Generator

Dupline ActiveX Server

Dupline DDE Server

Connection Dupline Field Bus Interactive Energy Software End-User

Sensors Fig. 3. Dupline data access.

Fig. 4. Demo building automation system.

leads to increase operational and energy efficiencies according to the energy efficiency requirements of EN 15232. Complex and integrated energy saving functions and routines can be configured on the actual use of a building depending on the real user needs to avoid unnecessary energy use and CO2 emissions. These functions include individual room control with communication between controllers, temperature control of distribution network water temperature regarding heating/cooling control, air flow, temperature and air humidity control concerning ventilation and air conditioning, as well as automatic or manual daylight control and occupancy detection for lighting. Moreover, the tool can be appropriately customized so as to provide information for operation, maintenance and management of buildings especially for energy management (trending and alarming capabilities and detection of unnecessary energy use). In addition, the proposed tool provides the opportunity, especially to the users of tertiary sector building, to increase energy efficiency, reduce costs and improve energy performance into the framework of ISO 50001 Energy Management Standard.  Step 3 – Parameters’ selection: based on the energy-efficient automation functions, a number of sensors that measure and record temperature, relative humidity, air quality, movement and luminance in the building areas are selected for the examined application of the case study building. In addition, controllers, such as switches, diaphragms, valves and actuators are correspondingly used.  Step 4 – Energy consumption simulation: The proposed automated system can monitor the energy consumption using the network of sensors and metering equipment (energy analyzer).

The data from building automation systems transfer through Master Generator to PC. These data are collected, stored and organized, enhancing the interactivity of building automation systems. The software processes data, providing averages, pick-load, statistics and graphs regarding electrical consumption and economic impact. Therefore, the user will be able to identify the weak points of his building and also the energy efficiency potential through real time monitoring and comparative analysis of energy consumption profiles from different time periods. This is a powerful tool for sensitizing users on their energy consumption, so as to mobilize them for its decrease.  Step 5 – Energy consumption optimization: Apart from analyzing the building’s energy profile, the proposed tool integrates control scenarios using optimization techniques which minimizes energy consumption and rationalizes the energy use in the highest degree. In this context, a deterministic optimization method is applied, based on control algorithms to achieve peak load reduction while still maintaining the building as a healthy, productive and comfortable environment for the building occupants. More specifically, the lighting level, temperature, relative humidity and air quality are the main parameters that are manipulated to achieve the required indoor conditions. The data on energy use profiles, including among others electricity bill data and peak demand, are used for the formulation of control algorithms. The proposed tool at this level minimizes the energy consumption of the building and assists energy users to save substantial amounts of money. Detailed presentation of the tool’s procedure, step by step, is analyzed in Section 3.

10

V. Marinakis et al. / Applied Energy 101 (2013) 6–14

Fig. 5. Interactive software’s procedure.

3. Pilot appraisal The presented system was applied to a supermarket building in Athens, Greece, which consists of five floors and a total surface area of 758 m2. In the following paragraphs, the pilot appraisal, step by step, based on the tool’s procedure, is presented. 3.1. Energy users’ profile The energy demand of the building is fully covered by electricity. The electricity bill ‘‘Tariffs B1’’ is offered to this company, taking into consideration the power demand charge and electric usage [62]. In particular, power demand is estimated at 12,0640 €/kW and electricity charge is estimated at 0.07185 €/kW h for the first 400 kW h per kW (recorded peak-demand) and 0.04760 €/kW h for the remaining. During the summer months, four A/C devices on each floor operate reinforcing the refrigeration. After the record of weekly energy profile by a portable electrical energy analyzer, peak demand was recorded of about 400 kW, with 100 kW concerned the A/C system. A summary of the main data concerning the case study building are presented in Table 1. 3.2. Identification of energy-efficient automation functions In this context, the aim is to reduce the A/C demand in 90 kW (max), especially during the summer peak hours. This action was based on recent guidance and decisions, according to which, customers of high energy consumption in Public Power Corporation

Table 1 The key data input for the building. Total surface area Power demand

758 m2 12,0640 €/kW

Electricity charge

0.07185 €/kW h for the first 400 kW h per kW 0.04760 €/kW h for the remaining

A/C system Total peak demand Peak demand for A/C system

20 A/C devices 400 kW 100 kW

(PPC) that achieve low consumption during the peak hours (11:00–14:00) in the summer months, are rewarded with a discount in their electricity bill [63]. To this end, the proposed software tool incorporates A/Cs’ controllable and programmable shut-off, which is short so as not being perceived by the energy end-user and not affecting the effectiveness and proper operation of A/C devices. 3.3. Parameters’ selection A temperature sensor controls the four A/C units on each floor and an energy analyzer counts the total electricity consumption during the building operation. 3.4. Energy consumption simulation The software provides the ability to receive, store and display electrical data from the energy analyzer, such as total energy

V. Marinakis et al. / Applied Energy 101 (2013) 6–14

11

Fig. 6. Interactive software tool.

consumption, voltage, current, active/reactive power, power factor and frequency, as presented in Fig. 6. Moreover, the system calculates the economic impact based on estimated energy consumption and PPC’s tariff. The elaboration and further process of the received data lead to the production of seasonal energy demand charts, as well as building’s annual, monthly and daily energy consumption profiles providing the energy end-users real time monitoring of energy consumption and clear mapping of their needs and energy saving potential. For example, the building’s daily electricity consumption data and the relevant diagram are presented in Fig. 7. 3.5. Energy consumption optimization The building’s operation is divided into two different time periods, according to recent guidance and decisions as described in the previous sections, namely peak hours and non-peak hours. Moreover, a number of different zones are defined during the peak hours, so as to reduce the A/C demand in 90 kW (max), especially during the summer peak hours. In this context, the proposed software tool incorporates A/Cs’ controllable and programmable shutoff. The control is repeated every 20 min so the temperature of each floor does not change much due to the deactivation of A/C units. In a general manner, the abovementioned procedure can be depicted in Fig. 8. A detailed description of the control algorithms and the parameters used is presented below:  Peak hours: The activation and deactivation of A/C units is based on the total building load’s control and each floor’s temperature. In particular, the temperature sensors on each floor have been set to predefined maximum and minimum values through software and the end-user has not the ability to control them. Moreover, the total building load divided into four zones, as presented below: – Zone A P 390 kW: The operation of all A/C stops automatically, regardless temperature sensors’ indication decreasing significantly the total building consumption. The control is

repeated every 20 min in order to turn on again some units, while maintaining desirable comfort. – Zone b 378–390 kW: A/C system is set on periodic operation. At the first time, A/C units on the last floor are turned on, controlled by the thermostat. After 20 min they are turned off and A/C units on the floor below are turned on, etc. – Zone C 366–378 kW: A/C system is set on periodic operation. At the first time, A/C units on the first, third and last floor are turned on, controlled by the thermostat. After 20 min they are turned off and A/C units on the remaining floors are turned on, etc. – Zone D < 366 kW: All A/C units operate according to the predefined maximum and minimum values. However, the user can turn on/off the units.  Non-peak hours: A/C units are being turned on/off according to each floor’s temperature. Their maximum and minimum values of temperature are determined by the energy end-user. In particular, when the floor’s temperature is: – Higher than the maximum limit => A/C units on the floor are turned on. – Lower than the minimum limit => A/C units are turned off. – Between the minimum and maximum limit => A/C units’ operation are determined by the end-user. 3.6. Operating cost assessment According to the above mentioned control scenarios, below is presented a chart, where the building’s operating cost profile between May–September for the time period of 2007–2009 is clearly depicted (Fig. 9) . One important point derived from this figure is the significant decrease of operating cost during the system pilot application (test period 2008–2009) as a result of the recorded peak-demand decrease. More specifically, the aggregated cash flow for the 25-year proposed investment is indicatevely illustrated in Fig. 10. The aggregated cash flow is based on the initial cost for the installation and operation of the automation system, the average anual energy

12

V. Marinakis et al. / Applied Energy 101 (2013) 6–14

Fig. 7. Building’s daily load curve.

30.000

Aggreagted Cash Flows (

25.000 20.000 15.000 10.000 5.000 0 -5.000

0

5

10

15

20

25

-10.000 -15.000 -20.000

Year Fig. 10. Aggregated cash flow.

Fig. 8. Optimization procedure.

17000 2007 16000

2008 2009

saving as derived from the system pilot application (test period 2008–2009) and the anual maintenance cost. The results revealed the significant potential for energy savings through the optional installation and operation of an automation system of sensors and meters for monitoring the A/C system operation and the optimization of energy consumption.

Cost (

15000 14000

4. Conclusion

13000

Nowadays, the building’s facilities are becoming more and more complex, and the needs for interaction among them increases. Therefore, an integrated system which includes remote control technology to enable energy end-users to monitor the energy consumption and control the operation of buildings’ appliances, as well as optimization functions is required.

12000 May

Jun

Jul

Aug

Month Fig. 9. Building’s monthly cost profile.

Sep

V. Marinakis et al. / Applied Energy 101 (2013) 6–14

The objective of this paper was to present an integrated system for buildings’ energy-efficient automation customized to the users’ requirement and building characteristics. The presented system was based on a prototype software tool. In particular, the combination of the proposed system with the optional installation and operation of an automation system of sensors and meters are a step forward for the simulation and optimization of energy consumption in the building sector. The contribution of the proposed system to the related scientific literature on the energy and environmental management of the building sector is summarized as follows:  Based on the energy data collection, storage and statistical information (e.g. averages, pick-load and graphs) provided by the proposed software, the end-users can identify the energy consuming sectors of the building through real time monitoring and comparisons of energy consumption profiles from different time periods.  The designed software tool can be compatible to the requirements of EN 15232, incorporating the appropriate building automation and control system functions.  The proposed tool systematizes the users of tertiary sector buildings’ routines, enabling in this respect the implementation of the ISO 50001 international standard in energy management.  Optimization of energy consumption by implementing appropriate control scenarios, which minimizes energy consumption and rationalizes the energy use in the highest degree. The interactive system presented above achieved significant decrease in the operating cost of A/C system in a tertiary sector building, while maintaining desirable comfort, in line with recent guidance and decisions for discounts in their energy bill. Finally, it should be mentioned that the proposed system could also be useful for the achievement of energy and cost savings for different types of load, such as lighting and heating with appropriate modifications. It has to be underlined that the proposed software tool does not intend to replace the already-developed systems, but to support energy end users through an effective energy and environmental management of the building, in accordance with the objectives of the newly conducted EU directives and regulations. Acknowledgements The authors would like to specially thank Mr. Alexio Adamopoulo (Electrical and Civil Engineer) for his significant contribution, as well as fruitful suggestions and comments to this paper. Mrs. Charikleia Karakosta wishes to acknowledge with gratitude the Alexander S. Onassis Public benefit Foundation for supporting her PhD research. References [1] European Commission. Communication from the commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions: 2020 by 2020 Europe’s climate change opportunity. COM (2008) 30 final. Brussels; 2008. [2] Intelligent Energy Agency (IEA). Policy pathways: energy performance certification of buildings – a policy tool to improve, energy efficiency; 2010. [3] European Commission. Proposal for a directive of the European Parliament and of the Council on the energy performance of buildings. COM (2008) 780 final. Brussels; 2008. [4] European Commission. Action plan for energy efficiency: realising the potential. COM (2006) 545 final. Brussels; 2006. [5] European Commission. Directive 2006/32/EC of the European Parliament and of the Council on energy end-use efficiency and energy services and repealing Council Directive 93/76/EEC. Brussels; 2006. [6] European Commission. Directive 2002/91/EC of the European Parliament and of the Council of 16 December 2002 on the energy performance of buildings. Brussels; 2002.

13

[7] European Commission. Commission Directive 2003/66/EC of 3 July 2003 amending Directive 94/2/EC implementing Council Directive 92/75/EEC with regard to energy labelling of household electric refrigerators, freezers and their combinations. Brussels; 2003. [8] European Commission. Commission Directive 2002/40/EC of 8 May 2002 implementing Council Directive 92/75/EEC with regard to energy labelling of household electric ovens. Brussels; 2002. [9] European Commission. Commission Directive 2002/31/EC implementing Council Directive 92/75/EEC with regard to energy labelling of household air conditioners. Brussels, 2002. [10] European Commission. Directive 2000/55/EC of the European Parliament and of the Council of 18 September 2000 on energy efficiency requirements for ballasts for fluorescent lighting. Brussels; 2000. [11] European Commission. Regulation (EC) No 2422/2001 of the European Parliament and of the Council of 6 November 2001 on a Community energy efficiency labelling programme for office equipment. Brussels; 2001. [12] European Commission. Directive 2010/31/EU of the European Parliament and of the Council on the energy performance of buildings. Brussels; 2010. [13] CSN, EN Standard. EN 15232 – energy performance of buildings – impact of building automation, controls and building, management; 2007. [14] The International Organization for Standardization (ISO). Win the energy challenge with ISO 50001. ISO 50001:2011; June 2011. [15] Kennedy S, Sgouridis S. Rigorous classification and carbon accounting principles for low and zero carbon cities. Energy Policy 2011;39(9):5259–68. [16] Wong JKW, Li H, Wang SW. Intelligent building research: a review. Autom Constr 2005;14:143–59. [17] Sgouridis S, Kennedy S. Tangible and fungible energy: hybrid energy market and currency system for total energy management. A Masdar City case study. Energy Policy 2010;38(4):1749–58. [18] Chwieduk D. Towards sustainable-energy buildings. Appl Energy 2003;76(1– 3):211–7. [19] van Beeck N, Doukas H, Gioria M, Karakosta C, Psarras J. Energy RTD expenditures in the European Union: data gathering procedures and results towards a scientific reference system. Appl Energy 2009;86(4):452–9. [20] Mechri HE, Capozzoli A, Corrado V. USE of the ANOVA approach for sensitive building energy design. Appl Energy 2010;87(10):3073–83. [21] Nair G, Gustavsson L, Mahapatra K. Owners perception on the adoption of building envelope energy efficiency measures in Swedish detached houses. Appl Energy 2010;87(7):2411–9. [22] Menezes AC, Cripps A, Bouchlaghem D, Buswell R. Predicted vs. actual energy performance of non-domestic buildings: using post-occupancy evaluation data to reduce the performance gap. Appl Energy, in press. [23] Popescu D, Bienert S, Schützenhofer C, Boazu R. Impact of energy efficiency measures on the economic value of buildings. Appl Energy 2012;89(1):454–63. [24] Wan KW, Li HW, Pan W, Lam JC. Impact of climate change on building energy use in different climate zones and mitigation and adaptation implications. Appl Energy, in press. [25] Buonomano A, Calise F, PalomBo A. Buildings dynamic simulation: water loop heat pump systems analysis for European climates. Appl Energy 2012;91(1):222–34. [26] Olofsson T, Mahlia TMI. Modeling and simulation of the energy use in an occupied residential building in cold climate. Appl Energy 2012;91(1): 432–8. [27] Costa A, Keane MM, Torrens JI. Edward Corry. Building operation and energy performance: monitoring, analysis and optimisation toolkit. Appl Energy, in press. [28] Siroky´ J, Oldewurtel F, Cigler J, Prívara S. Experimental analysis of model predictive control for an energy efficient building heating system. Appl Energy 2011;88(9):3079–87. [29] Karunakaran R, Iniyan S, Goic R. Energy efficient fuzzy based combined variable refrigerant volume and variable air volume air conditioning system for buildings. Appl Energy 2010;87(4):1158–75. [30] Danny HW, Cheung KL, Wong SL, Lam NT. An analysis of energy-efficient light fittings and lighting controls. Appl Energy 2010;87(2):558–67. [31] Rahman MM, Rasul MG, Khan MMK. Energy conservation measures in an institutional building in sub-tropical climate in Australia. Appl Energy 2010;87(10):2994–3004. [32] Wong JKW, Li H, Wang SW. Intelligent building research: a review. Autom Constr 2005;14(1):143–59. [33] Moradi Hamed, Bakhtiari-Nejad Firooz, Saffar-Avval Majid. Multivariable robust control of an air-handling unit: a comparison between poleplacement and H1 controllers. Energy Convers Manage 2012;55:136–48. [34] Zaheer-Uddin M. Optimal, sub-optimal and adaptive control methods for the design of temperature controllers for intelligent buildings. Build Environ 1993;28(3):311–22. [35] Trcˇka M, Hensen JLM. Overview of HVAC system simulation. Autom Constr 2010;19(2):93–9. [36] Fasiuddin M, Budaiwi I. HVAC system strategies for energy conservation in commercial buildings in Saudi Arabia. Energy Build 2011;43(12):3457–66. [37] Dounis AI, Caraiscos C. Advanced control systems engineering for energy and comfort management in a building environment – a review. Renew Sustain Energy Rev 2009;13(6–7):1246–61. [38] Clarke JA, Cockroft J, Conner S, Hand JW, Kelly NJ, Moore R, et al. Simulationassisted control in building energy management systems. Energy Build 2002;34(9):933–40.

14

V. Marinakis et al. / Applied Energy 101 (2013) 6–14

[39] Mathews EH, Botha CP. Improved thermal building management with the aid of integrated dynamic HVAC simulation. Build Environ 2003;38(12):1423–9. [40] Huang WZ, Zaheeruddin M, Cho SH. Dynamic simulation of energy management control functions for HVAC systems in buildings. Energy Convers Manage 2006;47(7–8):926–43. [41] Laudon K, Laudon J. Management information systems. Organization and technology in the networked enterprise, 6th ed. Prentice Hall Professional Technical Reference; Upper Saddle River; NJ; 2000 [ISBN 0130117323]. [42] Doukas H, Nychtis C, Psarras J. Assessing energy-saving measures in buildings through an intelligent decision support model. Build Environ 2009;44(2):290–8. [43] Avgelis A, Papadopoulos AM. Application of multicriteria analysis in designing HVAC systems. Energy Build 2009;41(7):774–80. [44] Doukas H, Patlitzianas K, Iatropoulos K, Psarras J. Intelligent building energy management system using rule sets. Build Environ 2007;42(10):3562–9. [45] Ma Z, Wang S, Xiao F. Online performance evaluation of alternative control strategies for building cooling water systems prior to in situ implementation. Appl Energy 2009;86(5):712–21. [46] Bichiou Y, Krarti M. Optimization of envelope and HVAC systems selection for residential buildings. Energy Build, 2011;43(12):3373–82. [47] Kusiak A, Li M, Tang F. Modeling and optimization of HVAC energy consumption. Appl Energy 2010;87(10):3092–102. [48] Adamopoulos A. What is the smart house?. Athens, Greece; 2009. . [49] Meyers R, Williams E, Matthews H. Scoping the potential of monitoring and control technologies to reduce energy use in homes. Energy Build 2010;42(5):563–9. [50] Bayindir R, Irmak E, Colak I, Bektas A. Development of a real time energy monitoring platform. Int J Electr Power Energy Syst 2011;33(1):137–46. [51] SBCI (Sustainable Building Industry Council). Energy-10Ò Software; 2008.

[52] EnergyPlus. Input/output reference: the encyclopedic reference to EnergyPlus input and output. The board of Trustees of the University of Illinois and the regents of the University of California through the Ernest Orlando Lawrence Berkeley National Laboratory, Urbana, IL; 2009. [53] eQuest. Introductory tutorial, version 3.63. James J. Hirsh & Associates, Camarillo, CA; 2009. [54] Azar E, Menassa CC. A decision framework for energy use reduction initiatives in commercial buildings. In: Jain S, Creasey RR, Himmelspach J, White KP, Fu M, editors. Proceedings of the 2011 winter simulation conference. Winter Simulation Phoenix, AZ; 2011. [55] Papadopoulos AM, Stylianou A, Oxizidis S. Impact of energy pricing on buildings’ energy design. Manage Environ Qual 2006;17(6):753–61. [56] Theodoridou I, Papadopoulos AM, Hegger M. A typological classification of the Greek residential building stock. Energy Build 2011;43(10):2779–87. [57] Greek Law 3661/2008. Energy performance regulation for buildings: measures for decreasing the energy consumption of buildings. Athens; 2008. [58] Wacks K. Home systems standards: achievements and challenges. IEEE Commun Mag 2002;40(4):152–9. [59] Ningbo F, Mingguang W, Xunsheng Z. Research of EIB and development of application module. Intelligent control and automation. WCICA 2004. 5th World congress, vol. 6; 2004. p. 5375–7. [60] Evans G, The EIA. Consumer electronic bus twisted pair network. IEEE Trans Consum Electron 1991;37(2):101–7. [61] Honda Y, Inoue M, Iwatsubo R, Sakanobe K. Protocol analyzer for home bus system (HBS). IEEE Trans Consum Electron 1990;36(3):586–92. [62] Public Power Corporation (PPC). Electricity prices for low & medium voltage. [63] Ministerial decision D5/HL/B/F.1B/1545/2008 – low 1161/B’/24.6.2008 ‘‘Providing financial incentives to consumers through PPC’ Bills to reduce power demand in July 2008’’. Athens; 2008.