Feasibility of UTS Smart Home to Support Sustainable Development Goals of United Nations (UN SDGs): Water and Energy Conservation

Abstract

The Sustainable Development Goals of the United Nations strive to maximize development needs, while minimizing environmental deterioration, without jeopardizing the needs of future generations. Nevertheless, due to urbanization, the escalating trend in natural-resource use, particularly electricity and water, is currently a crucial challenge for sustainable development. One of the promising options is the smart home, which is an extension of building automation with smart characteristics in monitoring, analyzing, controlling, and cloud computing with networked smart devices. Due to the lack of appropriate infrastructure and conscious consumption, its global adoption in the construction industry remains low. We present a technical feasibility of a multi-functional experimental smart home to support the Sustainable Development Goals of the United Nations in terms of water and energy conservation. The layered architecture of the cloud platform with an application program interface enables seamless integration of heterogeneous smart-home technologies and data sources. Use cases demonstrated its capacity to conserve electrical energy and water resources in support of the United Nations’ Sustainable Development Goals. Aside from that, the smart home’s electricity self-consumption of at least three autonomy days was confirmed with zero emissions and electricity bills, and a reduced supply-water consumption.

1. Introduction

The escalating trend in the consumption of natural resources, particularly electrical energy in the combustion of fossil fuels of power plants, has a negative environmental impact. Global warming, climate change, and depletion of natural resources are the three most pressing global issues. The Intergovernmental Panel on Climate Change (IPCC) reported in its Fifth Assessment Report (AR5) that the global urbanization rate has been continuing to increase [1]. Considering that only 13% of the global population resided in urban regions in 1900, urbanization has led to more than 50% of the population residing in urban areas. Additionally, it is anticipated that by 2050, this proportion will rise to 64 to 69% [1]. Cities are responsible for a significant share of global warming, with more than 44% of greenhouse gas (GHG) emissions [2]. It also deteriorated environmental sustainability and the depletion of natural resources, remarkably fresh water and energy.

In the new global development, GHG emissions and the scarcity of fresh water and energy resources have emerged as central issues for sustainable development due to the growth of the global population [3]. Sustainable development, which seeks to balance socioeconomic growth and environmental conservation, is one of the most effective means of addressing global challenges [4]. Although they occasionally conflict with each other, the measure maximizes development needs while minimizing, or at least reducing, environmental deterioration without jeopardizing the needs of future generations [5]. The 17 objectives and 169 targets of the Sustainable Development Goals (SDGs) were agreed to by member states of the United Nations (UN) when they adopted the 2030 Agenda for Sustainable Development in 2015. It aims to improve life on Earth within 2030 by solving social and environmental problems, particularly sustainability, and the rational use of the available resources (energy and fresh water) to meet current and future needs [6].

Urban activities directly contribute to the excessive consumption of natural resources in residential and commercial buildings. According to Lamb, the production of electricity and heat is recognized as one of the largest amounts of global greenhouse-gas emissions [7]. Based on the statistical record released by Malaysia Energy Commission [8], 84% of generated electricity by Malaysia is derived from fossil fuels. The issues of sustainability, energy security, the rapid depletion of global fossil fuel resources, and climate change have propelled the adoption of energy efficiency into their building construction strategic plans globally. Therefore, efficient energy usage is essential for conserving depleting energy resources and minimizing energy demand [9].

In 2019, Malaysia’s residential and commercial sectors accounted for 49.8% of the nation’s total electricity usage [7], mostly comprising buildings. Figure 1 shows Malaysia’s electrical energy consumption breakdown by different sectors [10]. As the data exclude consumption from industrial buildings, the actual proportion of electrical energy consumed in buildings will inevitably increase. Due to the high proportion of fossil fuels used for electrical power generation, the building sector is a significant contributor to GHG emissions. Residential buildings account for 30 to 40% of the global energy needs and approximately 40% of all annual anthropogenic GHG emissions [11]. Without energy efficiency (EE) measures, energy consumption could increase by 50% by 2060. It denotes the critical need for energy-saving and conservation measures to cut the GHG effectively and promote resource conservation in urban areas. A smart home (SH) enables the conservation of electricity and water resources. Ultimately, it denotes the rise of the smart home in a smart city and the beginning of the smart city transformation at the residential level. The bottom-up approach has numerous ways in which smart homes could contribute to the establishment of smart cities [12].

The smart home is a type of residential building automation that combines monitoring, analysis, cloud computing, and control capabilities with embedded technology of the smart things [13]. Smart things can be either devices or appliances which are intelligently integrated and interconnected via the Internet for different aspects of the smart home, including lighting, heating and cooling, computers, entertainment systems, security, surveillance systems, and home appliances. They can detect, decide, and actuate upon the smart-home environment. Therefore, people who live in an eco-friendly smart-home environment are happier, healthier, and more productive due to buildings’ comfortable, green, and sustainable architecture. Moreover, smart homes have numerous benefits of energy efficiency, quality of life, security and safety, and environmental conservation with strategies, as depicted in

Table 1. Benefits of smart homes.

Energy Efficiency	Quality of Life	Security and Safety	Conservation
Harnessing and storage of energy from renewable sources. Reduce, optimize, and make efficient use of energy consumption.	Maintain well-being and healthiness of occupants, complying with building standards with better market value and extended lifespan.	Prevent, detect, and mitigate the threats such as intruders, fire, gas leakage, CO2, and poor indoor air quality.	Conserve natural resources (energy and water). Reduce wastes and pollutants in the environment.

. Hence, the smart home concept has become increasingly popular and well-recognized. Consequently, it has become integral to modernization, conservation, and cost-cutting tendencies.

Despite decades of progress in SH technologies, their adoption in the construction industry remains low globally due to the lack of SH facilities and consumer awareness for conscious consumption of natural resources. Therefore, developing an SH with real environment settings as an experimental platform for smart home technologies is crucial. We present our experience developing our holistic and multi-functional experimental SH with a layered cloud framework architecture at the University of Technology Sarawak in Malaysia. The UTS Smart Home (UTS SH) serves various purposes, including teaching and learning (T&L), research and development (R&D), and a living lab of smart home technologies. Furthermore, the extensive and scalable architecture allows for secured and effective integration of various heterogeneous smart home technologies and data sources. Therefore, the university, students, researchers, and industrial partners can be most beneficial to these efforts. Furthermore, the use cases of the UTS SH in electricity and water-resource conservation are confirmed to support and promote UN SDGs.

The remaining structure of the paper is as follows: Section 2 introduces related works pertaining to various SH initiatives in various application domains, the use of SH in the construction sector, and experimental SH works from across the world. Section 3 depicts our developed experimental SH in Malaysia with its novel architecture design in hardware and software. In addition, the layered system architecture model and application program interfaces (APIs) are described. In support of the UN SDGs, Section 4 illustrates resource conservation in terms of electrical energy and water findings collected from the smart home. Section 5 presents our conclusions regarding the proposed smart home.

2. Related Works

The smart home was initially defined as adopting a unified communication system to integrate many services within a home [14]. It allows the home to operate efficiently, securely, and comfortably. To operate devices and systems in SH, however, Berlo et al. merged intelligence and automation [15]. In addition, it is characterized as a harmonized residence comprising a collection of networked devices and capabilities [16]. Therefore, a smart home is a residence in which fundamental household appliances and services are interconnected and can be remotely accessed, monitored, and controlled. Furthermore, a smart house must possess the following three characteristics: (1) an internet connection, (2) intelligent control, and (3) home automation. In contrast to previous definitions, however, Satpathy defines a smart house as one that enables the residents to stay free and in comfort with the aid of technology [17]. On the other hand, it can be further personalized as a designated home for the elderly, with its intelligent environment in response to its residents [18]. Based on the current trends and definitions reviewed, the smart home applies pervasive computing to deliver context-sensitive auxiliary services in remote home control and automation to its residents.

The global need for energy has steadily increased. Climate change and sustainability are the most challenging environmental concerns currently facing the globe. Human activities significantly contribute to global climate change. As a result, the current GHG emissions into the atmosphere are at an all-time high [1]. Buildings account for one-third of the global energy consumption and nearly 40% of all direct and indirect CO₂ emissions [19]. Nevertheless, buildings’ energy demand continues to rise due to several factors, namely the improved energy accessibility globally, the escalating trend of quantity and usage of electrical appliances, and the massive expansion of the building floor area worldwide [19]. It highlights the significance and urgency of a sustainable approach to counteract these global environmental challenges by adopting energy efficiency and intelligent technology in the smart home and smart cities.

Research on the automation of homes served as the foundation for the idea of smart homes. The primary topics include energy conservation, monitoring of indoor air quality and thermal comfort [20], heating ventilation air conditioning (HVAC), lighting, and water-heating control [21]; however, the emphasis has evolved over time. One of the studies conducted for this goal is the MavHome project [22], which aims to reduce building operational costs without compromising household comfort. The project anticipates the resident’s motions; estimates when heaters, lights, and electrical appliances are idled; and allows them to be used more frequently during those times. Additionally, the research forecasts the movements of the house’s residents. It determines when electronic equipment, lights, and heaters are not in use, thus maximizing their efficiency. The Amigo Project and Service-Centric Home [23] share a similar goal of lowering energy consumption through the development of software to guarantee interoperability between services and devices.

Smart home technologies are typically employed in load monitoring, demand-side management (DSM), and energy management system (EMS) applications with control algorithms for cost savings, comfort, safety, and resource conservation [24]. In contrast, an SH experimental platform with actual home settings is crucially required for research and development activities, including validating SH control algorithms of EMS, verifying conservation measures in SH, and ensuring the functioning and interoperability of SH with Smart Cities and Smart Grid. According to Cordopatri [25], due to different experimental settings for SH and heterogeneous smart devices and cloud platforms from different vendors, there is a lack of experimental SH infrastructure and a comprehensive cloud platform to support direct and comparable validation of the various control algorithms proposed.

Compared to the ASHRAE-100 standard, Fadi has undertaken a case study of energy performance monitoring on a smart home in Riyadh. In the category of family villas, the energy performance has dramatically improved by 37% [26]. A similar smart house test lab setting for energy and comfort management was also implemented by Antonio [25]. On the other hand, Myriam [27] investigated research of SH technologies applied to Smart Public Buildings in terms of use cases and prototypes based on an open-source platform. However, Espinilla [28] exhibited a smart home lab of the so-called UJAml to provide an environment where smart solutions of assistive technology might be deployed in the smart home in ambient-assisted living (AAL). Cristina [29] demonstrated the Internet of Things (IoT) in a smart-home automation system built on a Raspberry Pi board, ESP8266 chips, and an API. Regarding the platform model, Mokhtari [30] presented a novel layered architecture for a smart house powered by big data, complete with a RESTful API for data transmission. Two use cases utilizing platforms have been demonstrated in AAL and energy management.

From the literature reviewed, the previous works were primarily concerned with short-term testing and experimentation using only a few or a limited number of SH smart features. Different residential home settings were utilized for analysis and monitoring without applying comprehensive cloud computing and SH technologies. Additionally, none of them were discussed in relation to UN SDGs of resource conservation. To the best of our knowledge, a gap in the literature in regard to SH with real-environment settings for living lab and experiment platform exists to support UN SDGs in conserving natural resources. Furthermore, the interoperability issue is critical for SH to integrate heterogeneous smart devices with different communication protocols and data sources [31]. In this work, a holistic and multi-functional experimental SH was developed with a layered architecture of the cloud framework. The UTS SH serves various purposes, including T&L, R&D, and a living lab of SH technologies, benefiting the university, students, researchers, and industrial partners. Furthermore, the use cases of electricity and water resources’ conservation are presented to support and promote UN SDGs.

3. Materials and Methods

The University of Technology Sarawak (UTS), founded in 2013, is a public state-owned university situated in Sarawak, Malaysia. It is situated near the equator in Southeast Asia, at latitude 2°20′ N and longitude 111°50′ E. The temperature is often hot and humid (tropical) all year long, with annual rainfall averaging 240 cm. The monthly outdoor temperature is almost constant, i.e., the range of a minimum of 26–28 °C and a maximum of 31–33 °C, with an average temperature difference of 6.7–8.3 °C. The annual relative humidity (RH) value ranges from 58 to 66%. Additionally, it receives abundant solar radiation to produce sustainable solar energy [32]. The daily average, maximum, and lowest outside temperatures data of Weather Underground recorded from the weather station at Sibu Airport are shown in Figure 2 [33]. The tropical climate can closely affect the indoor environmental comfort conditions and energy consumption in buildings.

3.1. UTS Smart Home

3.1.1. Building Architecture Design

The architectural concepts of “Tiny Home” and “Container Architecture” are merged into the design concept of the UTS SH due to affordable costing, appropriateness to site location, and ease of construction of the smart home prototype. It has been defined that any residence with a gross floor area of less than 400 square feet is considered a “tiny home” [34]. It is widely adopted in the building industry of Western countries to achieve environmental sustainability through minimizing ecological footprints. In general, higher energy use results from living in a large home generated during and after construction in the building life cycle. Therefore, a sustainable alternative approach is necessary, given the growing concerns about environmental degradation and resource crises [35]. The shipping container architecture has demonstrated significant potential in terms of construction efficiency, economy, mobility, and sustainability [6]. The phrase “container architecture” refers to an architectural style in which the primary structural components are steel intermodal containers [34]. The container is structurally strong and flexible due to its modularity, which can be stacked, modified, moved, and extended. The container structural composition can be custom-designed as a functional space addendum to an existing building or even a complete building [36].

The UTS SH is designed as a studio-type living space with two 20-foot containers with steel frames serving as the primary building components. By offsetting the arrangement of containers with an asymmetrical plan, it creates a veranda for the main entry and an open decking at the back. The offset arrangement also lessens the monotonous impacts of the two containers’ cubical shapes when aligned in a symmetrical order. The site location was chosen mainly for ease of access by all visitors. It is close to the workshop areas and surrounded by woody plants that attract a variety of wildlife, including insects and birds. The cool and natural microclimate is created to reduce the building’s heat gain and cooling load. Furthermore, the main façade faces both the major road and the southwest, with such a building orientation intended to lessen heat gain and glare from the east and west’s respective early and afternoon sun. Additionally, the kitchen is oriented toward the southeast, while the living room and bedroom are facing the northwest for similar reasons, as depicted in Figure 3.

3.1.2. Functionalities of the UTS SH

The UTS SH was designed to resemble a hut or “longhouse” (traditional dwelling in Borneo Island) in a rainforest, while providing occupants with a safe, energy-efficient, high-quality, and cost-effective living environment. In addition, the multi-functional SH also offers the real SH environment settings, including hardware and software, for various purposes, including a living lab, T&L, and R&D, which benefit students, academics, and industrial partners for R&D activities. Figure 4 depicts the UTS SH, giving the interior view.

The UTS SH acts as a living lab to showcase and demonstrate applied SH technologies in several domains, including (1) quality of life, (2) health and well-being, (3) security and safety, and (4) conservation (energy and water). It is outfitted with various smart things, such as smart sensors for monitoring environmental variables, smart meters (water and electricity), smart actuators to adjust an optimal environment, and smart appliances (smart TV, smart washing machine, and smart blinds). These smart things are embedded with IoT capability with the seamless interconnection of the SH with the cloud. In addition, the UTS SH also serves as an experimental R&D testbed for real-time data collection and the validation of newly proposed control methods or algorithms. These experiments need to collect data from the physical world in a controlled and repeatable manner and perform data analytics with cloud computing in cyberspace for direct comparable results of control algorithms. Apart from that, it is designed with an open platform to create a variety of SH vertical applications that are highly integrated and interoperable. These include ambient assisted living (AAL), energy efficiency (EE) and predictive maintenance (PdM), and SG applications include distributed energy resources (DER), demand-side management (DSM), and demand response (DR).

3.2. System Architecture

The UTS SH is fundamentally a cyber–physical system (CPS) applied in the residential sector, with holistic monitoring and management of SH for energy efficiency, quality of life, safety, and resource conservation with minimal human intervention. It is based on a 7-layered architecture model [30] supported by Representational State Transfer (REST) APIs for communication between the physical (UTS SH) and cyberspace (the cloud). The smart-home system consists of (1) smart things, (2) networks, and (3) cloud computing, which can handle the present and future needs of Big Data applications. As depicted in Figure 5, the layered architecture consists of a physical layer, a fog computing layer, a network layer, a cloud computing layer, a service layer, a session layer, and an application layer that provides SH with distinct functionality for facilitating efficient data exchange and streamlining processing tasks in SH applications.

In brief, the physical layer of the SH consists of a variety of networked smart devices with sensing and actuation technologies to monitor and control an optimal home environment for occupants. The acquired data (variety and volume) are transmitted to the fog computing layer for data streamlining with limited storage and standard data format processing. The data are then uploaded via the network layer to the cloud computing layer for scalable data storage and processing. In the cloud computing, there are numerous data-driven services on the service layer that are utilized by cloud apps (UTS SH or a third party) in the application layer. These SH data-driven services make RESTful API calls over the session layer. The tiered architecture supports various SH vertical applications, with each layer having specific roles in addressing the IoT network complexity. For example, the physical layer is responsible for acquiring and converting signals from analogue to digital and vice versa. In contrast, the fog computing layer is responsible for facilitating data translation and reducing the system latency of data transfer to the cloud computing layer [37].

3.3. Smart Home Hardware

The UTS SH is furnished with a range of smart sensors and actuators, also called smart things, smart appliances, or smart devices. These are diversely organized and implemented at different zones within the SH to better demonstrate distinctive features and functionalities in several application domains, namely smart living, smart energy, smart security, smart kitchen, and smart access control, as depicted in Figure 6. The smart devices are either off-the-shelf products or custom-made designs.

3.4. Smart-Home Software and Cloud Framework

The smart home requires software to integrate SH services and cloud computing with machine-learning algorithms seamlessly. It encompasses the IoT cloud platform and Decision Support System (DSS). The IoT cloud platform enables SH data collection with cloud storage, computation, and visualization, while the DSS has various smart features based on AI/ML algorithms implemented.

3.4.1. IoT Cloud Platform

The web-based IoT platform is composed of PHP scripts hosted on web servers with a simplified IoT cloud model of physical, fog computing, and cloud computing. The scalable storage and computing resources are available in cloud computing. The interconnection between the physical SH and the cloud platform is accomplished via the fog computing layer. As described in the previous sections, it acquires data from different sensors and protocols in the physical space (the smart home). These data are transferred to the cloud in two mechanisms. For IoT-enabled (Wi-Fi-enabled) smart devices, data are uploaded directly to the cloud via Wi-Fi connectivity. On the other hand, those non-IoT-enabled devices can transfer data to the fog computing devices before uploading streamlined data to the cloud.

The smart hub, gateway, and data concentrators are built with small microprocessors, such as RPi or ESP32, to acquire and combine sensor data from different protocols (BT, ZigBee, Z-wave, etc.). The heterogeneous data are combined into a standard data stream for uploading to the cloud. In return, fog computing is also tasked with channeling command signals from the IoT platform (cloud) to smart devices for control purposes. At the cloud computing level, numerous servers or virtual machines are deployed for computation and storage purposes. It allows authorized access via user interfaces (UIs, such as smartphone Apps, web browsers, and tablets) and cloud software, as depicted in Figure 7. In that way, the users can have a holistic view of SH data for monitoring and controlling.

3.4.2. Decision Support System (DSS)

The UTS DSS has similar basic functionalities to the UTS IoT platform described in Section 3.4.1, except for some additional advanced cloud services. It uses a Python-scripted cloud application to facilitate various smart services for SH. With Flask technology [38], DSS is developed as a web framework with several intelligent features, namely monitoring and controlling, anomaly detection, and vertical applications. The GUI-based platform allows for the real-time monitoring and controlling of the SH, with a holistic view of monitored parameters of smart sensors, smart actuators, and even third-party data, such as weather and Smart Grid. Automated data monitoring with alert limits enables notification of the authorized personnel when the limits are exceeded. Furthermore, smart device control in the SH is accomplished by a DSS, either activating manually via GUI or automation of AI/ML-based control algorithms. The anomaly detection feature is used to keep track of device faults and ageing conditions. Once an anomaly is identified, an alert or notification will be issued for predictive maintenance (PdM) before any serious consequences [39].

The DSS supports good GUI-based visualization of monitored parameters (sensors, actuators, environment, and utilities), as depicted in Figure 8. On top of data monitoring, DSS also facilitates advanced AI-based services, namely real-time predictive maintenance and estimation of the remaining useful life (RUL) of an appliance [39], either by built-in ML algorithms or third parties, such as AzureML, with APIs. Furthermore, the smart control of the SH environment with smart decision-making solves optimization problems with human comforts and energy efficiency. Some smart features may require both acquired sensor data and third-party information. For instance, smart watering and energy features use open weather data and ML algorithms to optimize water consumption. At the same time, an RTP signal from the Smart Grid is required to achieve cost and energy savings with the demand response (DR) mechanism.

3.5. Sustainability Analysis of the UTS SH in Resource Conservation

Two use cases are presented to demonstrate and validate the capacity of the UTS SH to conserve natural resources (electrical energy and water resources), in support of the United Nations’ Sustainable Development Goals.

3.5.1. Smart Home Energy Management System (SHEMS)

The Smart Energy Zone of the UTS SH is designated to demonstrate smart features to conserve energy resource in terms of electricity generation from renewable energy and the efficient use of electricity by monitoring and managing of electrical consumption. Featuring a Smart Home Energy Management System (SHEMS), the UTS SH performed as a sustainable and zero-energy building (ZEB) based on an electricity self-consumption approach with zero GHG emissions and electricity costs by (1) hybrid electricity supply from the main grid and renewable resources and (2) efficient and reduced use of electricity consumption, as depicted in Figure 9.

To this end, SHEMS facilitates numerous energy-efficiency practices and AI/ML algorithms, such as the use of EE electrical appliances, appliance-fault and -ageing detection, anomaly detection, and demand-side management (DSM) strategies. Non-intrusive (NILM-based) and intrusive (ILM-based) types of monitoring tools or so-called smart meters are used to monitor and control the reasonable use of electrical energy in the SH. The NILM smart meter is custom-designed to disaggregate and classify electrical appliances in use based on a classification algorithm trained on AzureML of cloud computing [40]. Furthermore, it is also used to acquire aggregated load profile and consumption pattern for anomaly detection by using historical consumption data of the UTS SH, as depicted in Figure 10.

On the other hand, a custom-designed ILM-based smart meter so-called iCESocket is used to analyze faulty and ageing conditions of large-consuming electrical appliances (air conditioner, heater, and washing machine) based on AI/ML classification algorithms [41]. Furthermore, it is also used for demand-side management (DSM) practices and the demand respond (DR) of SG practices for remote and automatic switching control of connected appliances based on the optimal setting derived from AI/ML algorithms for EE and human comforts (thermal and visual). Examples include the optimum setting of the indoor air temperature of an air conditioner and the adjusting of the indoor lighting intensity without human discomfort and overconsumption of electricity.

Solar energy is among the most promising renewable energy sources. Through the chemical reaction of semiconductor materials, photovoltaic (PV) panels directly convert solar energy into electrical energy. It has several technologies, such as the solar heater, remote PV system, and building integrated photovoltaic (BIPV). Due to declining prices, solar PV systems are widely used in the residential sector [42]. The PV system can be operated either in parallel with the main grid for hybrid mode or off-grid (standalone) mode, depending on the availability of electricity infrastructure and metering tariff. In the UTS SHEMS, a hybrid PV–battery system with a capacity of 3 kW single-phase is used to supply electricity to the UTS SH, either from solar PV or the main grid. It is furnished with 8 PV panels (400 Wp per unit), a 3 kW hybrid PV Inverter, and a 300-Ah lead–acid battery bank with a storage capacity of 7.2 kWh, as depicted in the schematic diagram in Figure 11. To demonstrate the feasibility of the UTS SH in regard to electrical energy conservation, a detailed work was performed on electrical load profile and consumption pattern analysis, using smart meters and IoT cloud platform. The capacity of PV-generated electricity and energy storage was analyzed to validate its sustainability in electricity conservation based on the energy-balance concept of load demand–supply balance and electricity self-consumption of the UTS SH.

3.5.2. Smart Water Management (SWM)

Malaysia is classified as a country with high annual precipitation and domestic water consumption. With an average annual precipitation of 2400 mm [43], Malaysia is a typical tropical country with an abundant water supply. Nevertheless, the uneven rainfall distribution and overconsumption due to the highly subsidized domestic water supply have challenged the nation’s sustainable development. In the range of 209 to 228 L per capita per day (lcd) [44], the domestic water consumption of Malaysia is higher than the recommended target (165 lcd) set by the World Health Organization (WHO). Therefore, it foresees the challenge of water resource sustainability, and the use of rainwater has been widely accepted as a reliable alternative [43].

At the UTS SH, the Smart Water Management System in Smart Rainwater Zone is used for rainwater harvesting and reuse, data logging of rainwater storage and consumption, and remedial of water leaks. The system monitors and controls water consumption, rainwater recycling, smart watering, and smart farming. Hence, various smart technologies deployed include smart water meters, water sensors for detecting potential leak sources, rainwater storage tank and level sensors, and automated water control by water valves. The rainwater-harvesting system includes the rainwater collector and piping to harvest rainwater from the rooftop of the UTS SH. The collected rainwater is stored in a rectangular polyethene storage tank with a dimension of 150 cm × 150 cm × 100 cm and a capacity of 2250 L. The quantity and level of rainwater in the tank is closely monitored non-intrusively by a smart sensor based on ultrasonic technology. The acquired IoT sensor data are also uploaded and stored in the cloud server for monitoring and DSS services. Figure 12 depicts the UTS SH Smart Water Management System.

A smart water pump with a three-way valve can intelligently control watering operations for the garden and rooftop’s smart farming, apart from outdoor cleaning. The water source of the rainwater or treated supply water from the local water authority is controlled autonomously for non-potable use of outdoor cleaning and plant-watering operations. For instance, rainwater is prioritized to be used while it is abundant. However, the smart water pump will alter to source the supply water while the storage tank is running low (empty). It aims to optimize the recycling of harvested rainwater to sustain water resources. To do this, smart water valves are intelligently controlled for plant-watering operation. Based on Fuzzy Logic rules with the inputs of sensors and open weather data (current and forecast) acquired via API [33], the optimal frequency and duration of watering operations are determined. With the smart water meters installed, quantitative measurements for water consumption and resource conservation of the measures implemented in the UTS SH can be made. With that, water leakage and anomaly detections are implemented for efficient water consumption. Based on classification model trained in DSS, water-leak sensors and flow-rate information are used to determine the occurrence of water leakage without false detection, in real time. Furthermore, smart water meter data (current and historical) are used to perform an analysis with statistical data for anomaly or outlier detection algorithms (three-sigma rule and Interquartile Range IQR) and alert the user of overconsumption (outliers).

4. Results and Discussions

Two analytical studies were conducted to demonstrate the smart features of the UTS SH in resource conservation, with the results obtained from Smart Energy Zone and Smart Rainwater Harvesting Zone.

4.1. Resource Conservation on Electricity

4.1.1. Electrical Energy Consumption and Load Profile

The electrical-demand profile represents the potential domestic usage of the UTS SH based on a studio-type dwelling occupied by a couple or small family. During weekday office hours, the residents are assumed to be at school or working outside the home, while they remain home during evenings and weekends. The specification of electrical loads at the UTS SH is tabulated in

Table 2. Load characteristics and consumption profile.

No.	Item	Power P (Watt)	Qty N	Operation Hours, h	Wh/day
1	Lamp (outdoor)	12	2	6	144
2	Lamp (indoor)	12	6	6	432
3	Fans	80	1	6	480
4	Air-conditioner	800	1	4	3200
5	Television	100	1	2	200
6	Cooker hob	1000	1	0.4	400
7	Kettle	1000	1	0.4	400
8	Water heater	1000	1	0.5	500
9	Washing machine	1000	1	0.6	600
10	Refrigerator	100	1	24	2400
11	Miscellaneous (ICT and plug loads)	500	1	4	2000
			kWh (daily)		10.76
			kWh (monthly)		322.68

, which consists of load types, power ratings, quantity, and operation hours based on survey and analysis. Hence, the load curve and consumption profile are derived daily, weekly, and monthly. Equation (1) is used to calculate daily load consumption in Wh:

$$L={\displaystyle \sum }_{i=1}^k{P}_i.N_i.H_i$$

(1)

where P_i is rated power of appliance i, N_i is the quantity number, and H_i represents the operation hours of the appliance in a day.

Based on the calculation, the daily load demand of the UTS SH is 10.76 kWh, or a monthly consumption of 322.68 kWh. Therefore, the result complied with Ahmed’s findings of daily and monthly load demand of 11.50 kWh and 345 kWh, respectively, for a typical residential unit in Malaysia [45]. On the other hand, the load demand of the UTS SH is also empirically verified with real-time electrical data acquired from the smart meters and the IoT platform. Domestic electricity supply (single phase) is usually supplied to the residential units in Malaysia and is rated as 240 V and 50 Hz frequency. A NILM-based smart meter is installed at the entry point of the electricity supply of the main switchboard. The smart meter measures the electrical data by using a clamp meter without physical modification of electrical circuit wiring. The voltage and current readings are used to determine other parameters, such as power factor, power, and energy consumption. The readings are logged and stored in a cloud database server at an interval of 5 min. The UTS SH data can be accessed via the cloud platform for real-time visualization or downloaded as a primary data source in CSV format [41]. Figure 13 depicts the daily load profile (3 August 2022) of the UTS SH, with a total daily load demand of 11.41 kWh. It implies a 5.7% small discrepancy of the calculated daily demand compared to the actual daily load demand. These results are used as the load demand of the UTS SH for a further analysis of self-consumption with a peak daily load of 2.2 kW.

4.1.2. PV-Generated Electricity Supply

The UTS SH hybrid PV system requires proper sizing and evaluation to achieve energy sustainability for Zero Energy Building (ZEB). Analyses can be conducted by using both deterministic and stochastic (statistical) methods [46]. Although the latter approach is more reliable and realistic, it has high complexity in practice. Therefore, the deterministic method is more commonly adopted, with its simplicity and ease of calculation, for basic verification purposes. Furthermore, it assumes that load profiles and energy resources are kept constant, disregarding the statistical phenomenon of each system component [47].

In this work, the deterministic method was used to evaluate the energy sustainability of the UTS SH. Based on the energy-balance concept, the load demand needs to be fully supplied by the harvested electrical energy from the solar PV (with battery bank) for the self-consumption scenario [48]. Therefore, the expected electrical energy harvested from solar PV panels is determined principally by the solar radiation at the site. It can be conveniently calculated by using the Peak Sun Hour (PSH) value acquired from the Global Solar Atlas and World Bank Group [49], as depicted in Figure 14.

The PSH is an equivalent number of hours per day when solar irradiance averages 1.0 kW/m². In Malaysia, a typical terraced house has an average of 6.0 kWh as the daily electricity load. It has a range of PSH values of 4–5 h, with solar radiation ranging between 4.21 and 5.56 kWh/m² [50]. With an installed 1.9 kWp of a PV system and daily PSH of 4.5 h to acquire 8.6 kWh electrical energy daily, the energy balance and self-consumption are achieved by energy sustainability with zero GHG emissions [47]. Equation (2) calculates the total daily electrical energy acquired from the PV system:

$$P_{supply}=P_{PV}·PSH·{\eta }_{system}$$

(2)

where $P_{PV}$ is PV panel nominal peak power, PSH is peak sun hour, and ${\eta }_{system}$ is the overall system efficiency. When the PPV is known, the battery capacity can be derived by using Equation (3):

$$C_{bat}=\frac{P_{load}·N_a}{DoD·V_{rated}·{\eta }_{system}}$$

(3)

where $C_{bat}$ is the battery capacity (in Ah), $P_{load}$ is daily energy demand (in kWh), and $N_a$ is the number of autonomy days required. Moreover, DoD is the depth of discharge (in %), and $V_{rated}$ is the system voltage (in volts).

For the UTS SH, several datasets are collected online and at the project site. From Figure 13, UTS acquires a daily PSH of 4.71 h. The system efficiency (${\eta }_{system}$) of 80% has considered connection losses, dust factor, inverter efficiency, and charging efficiency. The optimum DoD is between 20% and 60% used for the optimal power production over the service life, while three autonomy days is reasonable for residential PV systems [47]. The total electrical energy acquired from the solar PV is calculated as follows:

$$\begin{gathered} P_{supply}=1.6\mathrm{kW}\times 4.71h\times 0.8 \\ =6.02\mathrm{kWh} \end{gathered}$$

From Equation (3), the number of autonomy days is derived from Equation (4):

$$N_a=\frac{C_{bat} · DoD· V_{rated}·{\eta }_{system}}{P_{load}}$$

(4)

The autonomy days are calculated as follows:

$$\begin{gathered} N_a=\frac{0.3\times 0.6\times 24\times 0.8 }{1.9} \\ =3.46\mathrm{days}\left(3\mathrm{autonomy}\mathrm{days}\right) \end{gathered}$$

The above calculation is based on the UTS SH’s specifications, i.e., a hybrid 3 kW PV–battery system with eight units of PV panels (400 Wp each) and a 300-Ah lead–acid battery bank (24 V) with a storage capacity of 7.2 kWh. The results show that the SHEMS system can generate peak electrical power of 3 kW from the solar PV system, far beyond supplying the peak load demand of 2.2 kW of the UTS SH. Furthermore, it has achieved energy sustainability with self-consumption of the energy balance between PV-generated electricity and load demand profile for at least three autonomy days available. Therefore, it is categorized as a sustainable and zero-energy building with zero GHG emissions and zero electricity bills incurred.

4.2. Resource Conservation on Water

Use Case of Smart Water Management in the UTS SH

For the typical automation of Smart Water Management, the water sensors are used to detect any potential leak of water in the SH. Once there is detection of water leakage, the system sends an event to the hub, triggering the “turn valve off” operation and master water valve to stop the leakage and notify the authorized person. AI/ML algorithms are performed to detect and conserve water resources for a smarter solution intelligently. By acquiring data from water sensors, smart water meters, and external weather data, cloud computing can compute the possibility of water leakage in the SH based on machine intelligence and determine the best time with the duration of the plant-watering schedule, as depicted in the GUI-based dashboard shown in Figure 15.

The economic and sustainable feasibility of Smart Water Management was analyzed according to water-consumption data for the period between April and June 2022. The water volumes are measured to analyze resource savings based on real-time data acquired from the smart water meters at the UTS SH. The local water board authority (SWB) charges the monthly water bill according to the amount of supply water consumed and the tariff, and water conservation due to rainwater consumption is determined.

Table 3. Water tariff of authorities in Sarawak, Malaysia (domestic) [51].

Minimum charge in any one month	RM4.40
1000 to 15,000 L	RM0.48 per 1000 L
In excess of 15,000 liters, but not exceeding 50,000 L	RM0.72 per 1000 L
The excess over 50,000 L	RM0.76 per 1000 L

depicts the water tariff of local authorities in Malaysia for domestic water consumption [51].

Table 4. Water-resource savings of the UTS SH (monthly).

Total number of residents:	2
Water meter reading 1, (supply water):	9.1 (‘000) L
Water meter reading 2, (rainwater):	2.5 (‘000) L
Total water consumption (per household):	11.6 (‘000) L
Daily water consumption (per capita, lcd):	193 liters or 193 lcd
Total water saving (liters):	2.5 (‘000) L
Total water saving (in percentage):	21.6%

illustrates an analysis of monthly water consumption at the UTS SH, according to the residential tariff structure of Sarawak, Malaysia. The monthly volume of water consumed is approximately 11,600 L, with a total savings of 2500 L, or 21.6% from the harvested rainwater. In terms of per capita daily water use, the UTS SH has an improved level, at of 193 lcd, compared to a prior study in Malaysia [44]. However, it is still greater than the WHO-recommended goal of 165 lcd. Therefore, water-resource sustainability cannot rely just on the reuse of rainwater; it must also rely on other aspects, such as customers’ conscientious usage, the government’s water-supply subsidy policy, and smart-home water-efficiency techniques.

5. Conclusions

Urbanization has severely challenged the sustainability of the environment, particularly the global conservation of energy and water resources. In this paper, we presented a technical feasibility of the UTS experimental smart home to support UN Sustainable Development Goals regarding water and energy conservation. The UTS SH is based on the layered architecture of cloud platform with APIs that enable seamless integration of heterogeneous smart home technologies and data sources. The use cases revealed its capability to conserve electrical energy and water resources with significant results. With intelligent water management, the rainwater harvesting and recycling has demonstrated a reduction of monthly water consumption by 21.6% or 2500 L, without compromising the needs of consumers. Although there is an improved water consumption of 193 lcd as compared to the Malaysia domestic water consumption of 209 to 228 lcd, the figure is still greater than the WHO-recommended goal of 165 lcd due to other factors, such as customers’ conscientious usage, the government’s water-supply subsidy policy, and smart-home water-efficiency techniques. Therefore, water-resource sustainability cannot rely solely on the reuse of rainwater; it requires a holistic approach that includes several practices and policies. Furthermore, the UTS SH with SHEMS has demonstrated the realization of the smart home as a ZEB and its capability of supporting electricity self-consumption for a minimum of three autonomy days with zero GHG emissions and electricity bills. Consequently, the presented use cases and analytical results in resource conservation have confirmed the UTS SH’s capability to support and promote UN SDGs. Future research can incorporate other AI/ML control algorithms to improve its resource-saving and efficiency results further. The multi-functional UTS SH with real-environment settings and facilities is mostly beneficial to university students, academics, and industrial partners for R&D activities in the field. With a bottom-up approach, smart cities can be realized through smart homes for the next generation’s resource-usage efficiency and sustainability.