Fuzzy rough set based sustainable methods for energy efficient smart city development

Abstract

The lightening system inside the residential or commercial building consumes the highest electrical power. For an energy efficient smart city development, some sustainable and low power consumption methods need to be explored. In this direction, we proposed solar energy based auto-intelligent LED light controlling system that uses wireless sensor network (WSN) with computation and control model for LED on/off and dimming of LED lights inside the building area. The WSN is employed with some sensor devices that sense and gather ambient context information which is transmitted to computation model. LEDs get power supply from photovoltaic solar panel systems that have inbuilt battery banks. Fuzzy rough set is a simplification of a rough set, obtained from the normalization of fuzzy set in a approximation of crisp value. Fuzzy is utilized for analyzing the energy consumed in the system additionally. Performance evaluation of proposed Auto-intelligent LED system is carried out based on the comparative analysis of energy consumption of ac-grid system with solar energy based dc-grid system. Result analysis shows that proposed system saves 78% of energy consumption as compared to the traditional AC power grid system. The proposed DC power grid system presents 3% of voltage drop and maximum power loss of 1.25%. The statistics of battery charger and LED drives are also represented experimentally.

Keywords

Energy efficient wireless sensor networks power consumption artificial intelligence (AI)smart grid fuzzy rough set

1 Introduction

In recent years, the growth of the oil and gas industries has become more predominant and forms an integral part of the day to day life. The oil industry also known as petroleum industry explores, extract, refine, transport and advertise petroleum products. Fuel and gasoline are the major components of the oil and gas industries [1, 2]. It is an advanced level of global industry refines and export oil and gas products across various parts of the international market. This enables the users to easily avail services with lesser complexity measures. For the past few decades, the hype of oil and gas industries continuously raises with its drastic development across various sectors. This rapid growth of modern digital systems acts as a key enabler for oil and gas industries with more advanced digital facilities. In the meantime, prompt urbanization in recent years results in tremendous growth of the urban population. The amount of the human population across urban areas increases every year in a drastic manner. This explosive growth of the population gives rise to the advent of modern vehicles and transportation facilities. This creates an increased demand for fuel supply with more emphasis on various measures of fuel extraction and consumption processes [3]. The concept of minimal energy conception with renewable energy resources is the progressive revolution of oil industries specifically designed to offer improved fuel facilities for increased population demands. This includes production, manufacturing and energy management across various industries such as coal, mine, gas, etc. In simple terms, the world is growing faster and the use of advanced techniques such as data mining can enhance the progress of industrial applications with minimal energy requirements. Further, the evolution of the IIoT is the most advanced level of IoT applications that concatenates various advanced approaches such as artificial intelligence, big data analytics, cloud computing, cybersecurity, and several other prominent techniques across a single stream of oil and gas industries [4, 5].

At present, the safety-critical applications such as gas and oil industries have started migrating towards data mining solutions as it simplifies the complexity of various system operations. With a considerable growth of modern technologies, the oil and gas companies have started replacing their traditional legacy systems with modern data analytics techniques. In general, the oil and gas industries have considered safety-critical applications as the process of extraction of fuels and gases involves numerous life-threatening operations. For instance, digging out of fuels from the ground and transferring it from the refinery to the customers is the most complex process and requires careful consideration of various factors such as transportation, drilling utensils, transportation, and logistics. Each factor is highly dangerous in nature and requires potential attention due to the reason that each factor is more critical than the other. To minimize the risk and simplify the complexity of most of the oil and gas industries have started adopting modern data mining solutions. Thus the use of data mining gradually decreases the risk associated with every operation and assists in the development of a sustainable business model [6, 7]. In oil and gas industries the real-time data is collected and analyzed through which the state of the refineries are monitored and analyzed in an effective way.

The data collected from the oil and gas industries are further analyzed to make real-time business decisions. This assists in improved safety, reduced equipment failure, reduced system downtime, reduced power wastage, and several other system performance measures. As a result, the risk factors associated with the oil and gas industries are automatically monitored and evaluated with no or less human interventions. Since the personnel of oil and gas industries work under real-time environment and perform numerous operations a huge volume of data is generated every second. In addition, the data generated from these industries are more complex and unstructured in nature; thus, the process of data management remains to be the most difficult process for oil and gas industries. This provides an opportunity for the progression of more advanced data mining techniques to be used across the IIoT environment.

The use of data mining in oil and gas industries is comparatively more important in present-day scenarios rather than in the past decades. Data mining is the process of collection and analysis of the huge volume of the data with the intent to extract meaningful patterns and relationships between the datasets [8]. The major objective of the data mining approaches is to improve and optimize the business process with efficient use of sensor data. It makes use of data mining algorithms to extract non trivial information (previously unknown) from the huge volume of sensor data then performs data analysis and provides easily comprehensible results. Through the use of the various relationships between the data objects and extracted patterns, it provides an efficient real-world data model. The modelling of fuzzy set to detail gathering and processing are normally fruits of interfacing data which has numerical content which includes labels of linguistic data containing numerical values in an environmental interpretable way. Therefore, a conclusive derivation can be formed utilizing fuzzy set initiated from fuzziness of mathematical formulation study, but it produces a normal platform of fuzziness for various other areas of sciences. Fuzzy set theory is vital of various links in a chain of different tools, gathered with the intension of handling with various scenario of knowledge and languages. As fuzziness is a scenario of present work in real world, the theory of fuzzy brightened up by utilizing linguistic models that used to handle as tool to known language in turn.

This data model assists in an effective business prediction and strategic decision making processes through which the coal and gas industries can be easily modernized. To solve a business problem, data mining enables an organization to develop various useful models such as clustering, classification, time series, and many more. Usually, the mining industries are associated with numerous processes such as the discovery of ore or mine deposits, finding the strike length of the development of the rocks to perform mining from underground pits, dissemination of end products across manufacturers, etc. The use of data mining can greatly enhance this process by providing facilities such as machinery analysis, tracking energy usage constraints, and several other anomalies related to production, and consumption [9, 10]. In particular, data is a crucial part of the oil and gas industries as it requires improved accuracy and efficiency measures. A slight deviation with accuracy can often result in considerable economic fluctuations. Thus, data mining has become a crucial part of the coal and gas industries [10].

In the oil industries, the concept of data mining can be used across three cases such as upstream, midstream and downstream. In upstream data mining is used for seismic data analysis through which the crashes in production can be reduced effectively. Data mining in midstream performs real-time transport data analysis using data logs. At the downstream, the data mining techniques are applied to optimize and automate gas stations with minimal financial risks [11]. Currently, data mining models have become the most essential part of the gas and oil industries. This is because of the reason that these industries repeatedly perform trade-off on financial and energy markets. Since data mining can collect and analyze trading information it provides an efficient solution for various risk factors associated with the unstable commodity markets. These further results in various advantages such as identification of relationships between commodities reduced transaction costs and improved prediction of transaction risks. Such that the trading companies can easily predict abnormal trading behaviors and normalize financial markets.

Data mining for mining industries has various advantages such as improved production accuracy, efficient sensor data integration, enhanced business management processes, faster data exploration, and production. In contrast, data mining is the major discipline of computer science when used can highly influence the growth of the energy industries. In the future, this technique could pave the way for advanced prediction and forecasting models. Thus, the mining industries can make effective business strategies with lesser investments.

Regardless of the successful efforts, the main problem is still existent with the oil and gas industries which are listed as follows:

These industries utilize a huge amount of computing power for data collection and storage processes (databases and warehousing). Thus energy consumption remains to be a major problem with an emphasis on low cost, portable electronic sensors, and computer memory. The data has to be stored in such a way so that the decision-makers can use it later for analysis and other requirements.

Data processing and distribution remain another major problem across existing coal and gas industries. Thus, concentrating on the automation of system processes, standard presentations, and streamlined data transfer at low cost and efficiency can improve data processing and distribution processes.

Further, the process of transformation of data into information with intent to make business decisions remains to be the most complex process across existing systems.

This paper is aimed to provide enhanced business models equipped with data mining for the coal and gas industries. Although the concept of data mining has reasonable benefits for mining industries it remains at an early stage of development and requires greater improvement. Thus, the proposed approach is developed with an intention to solve existing drawbacks and provide an effective solution for the coal and gas industries. One of the key applications of the proposed approach in mining industries is to overcome the problem of energy consumption and increased cost. In addition, the proposed approach improves the efficiency of the data analysis process and enables it to provide appropriate responses to the changing business environment.

2 Related works

Smart city is practically a new concept and is a promotion of digital city and city with sustainability by Yigitcanlar [12]. Moreover, it is been used more often, particularly later than 2013, when it crosses the occurrence of citations of different expressions including sustainable city Yigitcanlar [13]. Living aside the discussion about its inclusion in recent years, there is a drawback what the smart city is about Angelidou [14] and Caragliu et al. [15] theoretically explained the concept of smart city with some characteristics such as a) an improved administrative and efficiency in economy that makes the enhancement in cultural and society by using the infrastructure; b) development of urban region based on the importance to the business; c) giving importance to the fact that the social characteristics of various kinds of social enclosure in services in public; d) an prominence on important role of highly efficient technology and industries in long period growth; e) perception to take a close look to the social function and capital relation in developed city; f) an idea to take sustainability in environmental and social aspects for development of smart city. Various authors have pinpointed the key components for smart cities such as; clever people, keen economy, clever mobility, smooth governance, cool environment and keen living Lazaroiu and Roscia [16]. Moreover, the definition of smart city is far from intellectual people in city, statistics cities and clever cities, as its technology will be used in favors of people and in systems favor.

The development of smart cities as suggested by Marsal-Llacuna et al. [17] has to look at the previous experience of friendly environment and civilised cities, enhancing sustainability and people’s quality of life and degree of factors involving in technology. Lazaroiu and Roscia [18] explained that it should symbolise and community with technology, unified, sustainable, luxury, smart and safe. To precisely watch how the smart city works practically, smart city utilize cities data for management of traffic, statistics on energy consumption, security and municipal service optimization Harrison et al. [19]. Several algorithms were analyzed by Senthil Murugan and Usha Devi [20, 21] for processing large amounts of data and being used optimization techniques which would be a great advancement in the field of energy optimizing. Latest reality of smart city development is to promote an increase in new suppliers for management of services in urban areas using technological resources Carvalho and Campos [22].

The work proposed by Schaffers et al. [23] is lately highlighted by Kramers et al. [24], pointed to the necessity of smart city: a) creating a higher network conFigureuration for broadband that enables digitized applications, and; b) stimulating big scale processes involved innovation that creates application. Concepts of smart cities have been applied in some cities that enable them to enjoy the benefits. Barcelona defined smart city as the intensive high technology and enhanced city that links people, information’s and elements in the city using brand new technologies to make city sustainably greener and economical Lee et al. [25]. In Doha, practice of smart city is the interaction between the economic activities and urban technologies Conventz et al. [26]; in other case of Brisbane, it to manipulate smart technologies into better urban and design of space practice Pancholi et al. 2015 [27].

Nam and Pardo [28] splits smart city into different dimensions such as: a) Technology (infrastructures consisting of software and hardware); b) Population (literacy, diversity and originality) and; c) Institutions (policy governance). Pertaining to this, smart city development is based on the technological investment, growth in population and cost of living that produces the development of sustainability, developing managements that takes responsibility of resource allocation and providing institutions to subsidise with innovation and good services to citizen, enhancing political participation Caragliu et al. [29].

When considering the cities, in order to get better knowledge of sustainability, one must consider the meaning of urban sustainable development Dizdaroglu and Yigitcanlar [30]. In turn, the process of exploitation of resources can change, direction of investment, development in technology and change in institutions are reliable with future and current needs which are analysed by WCED [31]. Sustainable cities became most famous in the 1990 s that Roy [32] represents the correlation between socio-economic and environmental activities from the arrangement of gauges of each of these elements Ahvenniemi et al. [33]. The present phenomenon is to get to know about these three issues to know about sustainable cities. These factors are focused by some of the authors. In meadows case (1999), generation of energy, pollution in the city, energy and water consumption was proposed to be included for sustainable smart cities, whereas Rode and Burdett [34], who proposed for the inclusion of social and economic factors such as social equality and healthier environment Jong et al. [35].

Taking all the aspects into account, Hiremath et al. [33] states that the development of urban sustainability can be achieved with a balance concerning urban area development and environment protection with the income equity, job opportunities, accommodation, basic amenities, infrastructure and urban area transportation. The area of interest in smart cities and contiguous concepts is related to several factors such as large population living in cities, change in climate, limited natural resources, globalisation and enlarged competition. Keeping these factors in mind, smart cities have to provide enhanced and highly customised services for people Angelidou [14]. Conferring to Dhingra and Chattopadhyay [34], sustainable smart cities has to achieve certain goals in a flexible, consistent, accessible, such as:

Enhancing its citizens quality of living;

Providing larger employment with better economic growth;

Enhance well-being of its people by confirming access to social services;

Create an responsible environment and sustainable method for development;

Confirming service delivery as efficient as possible and improvising infrastructure facilities such as transportation, drainage, supply of water and issues related to environment and other various utilities;

Managing environmental and climatic issues and;

Giving efficient regulatory and local governance that provides reasonable policy.

When its issues relating to environmental concern in smart cities, the discussion will be more administrative in nature, taking international resolutions into account and advanced solutions to difficult challenges in urban areas. Same author suggested that four different attributes play a role in sustainable and smart cities: (a) sustainability; (b) people’s quality of life; (c) aspects of urban living, and; (d) intelligence. These are investigated under four various themes: (a) Society; (b) Wealth; (c) Surroundings, and; (d) Governance. Development of urban region based on knowledge considers these themes were presented by Yigitcanlar and Velibeyoglu [12], which makes a new concept for the smart cities development. A familiar concept, eco-smart city, proposed that city must have a healthy ecology, technical advancement and having industries with productive economy and friendly environment, having tuneful and responsible systematic culture by Yigitcanlar and Lee [11].

The theory of “sustainably smart cities” has newly proposed as an answer to earlier blame on smartness and urban sustainability Ahvenniemi et al. [30]. Fundamentally, the emerging strategy is the combination of elegance and sustainability of urban areas highlighting all the aspects should be simultaneously considered. Its growth from both aspects as a) feedback to the criticism made on smart city clarification that are inconsistent to sustainability, and b) as consequence to find the needs of present highly modernized cities more systematically than the sustainability from traditional strategy.

Logically, set of defined indicators are used regularly by the cities to enumerate their goals and follow certain procedures to observe the growth towards their targets Munier, [35]. Smart cities make a note of the indicators to track the progress internally and communicate their growth Dameri, [36]. Having a larger number of urban data, selection of most prominent performance indicators should be chosen by the city manages to get a close look at the performance of various areas in the cities. Yang et al. [37] mentioned a improvised version of intuitionistic fuzzy PROMETHEE II. Liao and Xu [38] mentioned the PROMETHEE inclusion in IFS context. In this procedure, initially they obtained the Decision makers estimation ranges of the substitutes over the scenario by numerical values of crisp. Such that they estimated the degree of membership which is obtained from every pair of substitutes over variation criteria and then utilized the functional preferences. They monitored the degree of non-membership components as equivalent to degree of membership function. For preeminence condition, they use the ranges of fuzzy intuitionistic outranking movement by a component, which is gathered by the substituted ranking.

3 Proposed work

3.1 Overview of DC power grid solar energy based LED lightening system

The usage of lightening in any official or residential is immense, energy saving in term of lightening-account contribute energy efficient step in constructing sustainable smart city. For the development energy efficient sustainable smart city, rather than using non-renewable source of light, some renewable energy resource must be used. Currently, solar energy based renewable energy system are much in demand because of low cost, easy installation, and long life span. In recent year, light-emitting diodes emerge as energy saving, low maintenance, long lifespan and highly efficient alternatives to various traditional sources of light including fluorescent lamp, incandescent lamp, and halogen lamps. LED luminaires have built in DC nature, earlier LED lightening system consist of AC-DC converter which is used to convert AC power supply into DC supply that need extra power for this conversion.

Our proposed intelligent lightening system (ILS) will work on the principle of solar energy with LED lightening system. LED is compatible with life span and DC supply of solar panel system. Figure 1 shows the framework architecture of proposed flow. Our proposed system is composed of a data acquisition module, WSN based communication system module, control system module and Photovoltaic solar panel system module. In our proposed system, sensing information sensed from various sensor devices is transmitted to the computation system via wireless sensor network communication system. Based on the processed and computed information control system perform turn on/off and dimming of LED lights. LED light illuminated through the electric power generated by photovoltaic solar panel cells.

Fig. 1

Framework Architecture of Proposed System.

3.1.1 Data acquisition

To collect ambient context information like humidity, occupancy, temperature lux level, motion etc. we use PIR (Passive Infra-Red) motion sensor that detect inhabitant movement activity and LDR (light dependent resistor) light sensor that detect light intensity from LEDs and from outside daylight.

3.1.2 WSN based communication system

The transmission of received sensing information to control system models is carried out by wireless sensor networks that incorporate various wireless sensor nodes and WSN base stations. The sensor node is equipped with memory, a microcontroller, ambient sensor, power supply and wireless radio transceiver.

3.1.3 Control system model

It is not possible to control the light intensity of sunlight but in order to measure the luminance and ambient environment of the user’s workplace LDR sensor sense light intensity of both sunlight as well as LEDs. A closed loop approach is used to control and adjust light intensity based on sensor inputs and user surroundings. The systematic view of the control system model is represented in Fig. 2.

Fig. 2

Control System Model.

The sufficient and required lighting should be in between 300 lux to 500 lux. The digital output ln with reference to user’s workplace set point Xn is computed by using this closed loop approach. The error rate is measured by taking the difference between the Xn and light signal NDC received from LDR sensor. The light intensity of LEDs that meet user requirement should perform with following two controlling step:

LDR sensor based LED lighting control: Consider n number of sensor nodes where n = 1,2,3, ... .17 with utility service function Ln(Ts, Pn, ln) that shows the relationship among surrounding motion ambience Pn detected by PIR sensor, controlling time duration Ts, and LED digital output ln on specified intensity of light measured based on sensing sunlight and LEDs light using LDR sensor with reference to user workplace Xn. This utility service function Ln is defined in Equation 1: $\begin{matrix} L_{n} (T_{s}, P_{n}, I_{n}) = {P_{n}, I_{n} (T_{n} < 7 : 45 AM, T_{s} > 5 : 00 PM) I_{n}} \\ (7 : 45 AM ⩽ T_{s} ⩽ 5 : 00 PI) \end{matrix}$ (1)

The LED lighting system automatically dimmed or turned on/off during day hours from 7:45 AM to 5:00 PM.

PIR sensor based LED lighting control: This auto-intelligent LED lighting system will turn off automatically after the office hours while turns on/off during this time in residential area as well as workplace of office staff that stay after office hours based on the PIR sensor motion detection ambience illumination. This situation can be expressed by following Equation 2: $\begin{matrix} L_{n} (P_{n}, l_{k}) = \\ {l_{n}, (P_{n} ⩾ 1) 0, (P_{n} < 1) \end{matrix}$ (2)

When any human activity and motion detected by PIR sensor (P_n⩾ at workplace or residential place then corresponding values of using the LDR sensor is transmitted to the controlling system via WSN communication from sensor node and base station WSN. But during night hour only PIR detected area with (P_n⩾ get illuminated while other areas where (P_n< will turned off with (l_n =. The Schematic flowchart of LDR sensor based LED lighting control is shown in Fig. 3.

Fig. 3

Solar Energy based Photovoltaic Solar Panel System.

The received output is then transmitted to the DALI controller via RS-232 serial communication protocol to control the LED lighting system. The sensor output is also transmitted to the cloud system for further reference and statistical analysis of energy consumption and energy saving in the whole city.

3.1.4 LED lightening system

LEDs of lighting systems can be connected serially, paralleled or in a mixed fashion. In order to avoid complexity of parallel or mixed arrangement we are using serial LED connection arrangement and the input design parameter is defined as follows:

Input voltage supply of 24 Vdc.

Output supply voltage of 68 Vdc with 700 mA and 47.6 W.

50 KHz of switching frequency and 25% of input ripple current

The designed equation of LED driver is defined in Equations 3 and 4: $c_{D} = 1 - \frac{V_{B}}{V_{L}} = 1 - \frac{24}{68} = 0 .$ (3) $L_{V} = \frac{V_{B} c_{D} T_{ST}}{Δ I_{V}} = \frac{24 \times 0.65 \times 20 μ}{0.5} = 624 μ$ (4)

Where, represent minimum duty cycle value for the converter, represents minimum voltage level of battery, shows voltage level of LEDs connection with as inductor value, switching time period T and L is the input ripple current of the converter.

3.1.5 Fuzzy rough set

Components with same characteristic consists a set. Based on the classical theory of set, there should be a element which must include in the group or be fully involved from it. Moreover, this includes a numerous antinomies. Initial such antinomy was given as in Equation 5 below; $Let T = {a | a \notin a}, then T \in T \Leftrightarrow T \notin T$ (5)

Researchers gained attention in notation which are vague, like the components beauty. A component cannot be identified. Moreover, the description of beauty is involved to be not precise. Same like every concepts utilized in natural vague languages. The basic theory proposed consists of components that belong to a group of particular degree n, where, 0 ⩽ n ⩽ 1. The scenario of fuzziness was utilized to involve the regions of boundary. The idea of minimal approximation, maximum approximation and boundary section were utilized. The rough set which involves it has non-empty boundary portion. Hence, the theory of rough set associates the two basic components, i.e., vagueness and uncertainty.

3.2 Solar energy based photovoltaic solar panel system

Solar energy is one of the most promising and alternate renewable sources of light that can be converted into electricity by using photovoltaic cells made of semiconducting material like silicon (Si). For installing photovoltaic solar panel systems, the correct size photovoltaic cell panel can be determined based on the level of solar rays of the installation location and this solar radiation level can be determined by using SUNDATA software [39 –41]. The preferred radiation level for this system is 3.57 kWh/m2 determined in the June month. The schematic representation solar energy based Photovoltaic Solar panel System is shown in Fig. 3.

The installation of this solar panel system can be performed either in the isolated remote area or over the rooftop of the building. The generated electricity is stored in the electric storage system known as battery bank, and this battery is charged periodically.

4 Simulation results

To analyze the performance evaluation of proposed self-tuning solar energy based LED lightening system we designed and developed PI-controller with closed loop that keep the tracks of set-point lux mention for room brightness. For experimentation we use different cases of office environment that control the light intensity from 530 lux –360 lux. In case 1, the illuminating value of the table is measured without controlling the operating brightness of luminaires. This illuminating value is considered to be higher than most eye relaxed levels in addition to the illumination value of daylight contribution. Case 2 referred to as most eye relaxed level in which illumination value of table is adjusted and maintained based on the presence and absence of employee using motion detection PIR sensor. For instance, in the absence of a user from its workplace the value of illumination is decreased and as the presence detected value is adjusted based on the preference and comfort level of the employee. In case 3 and 4, users can have the freedom to select their own lighting preference based on the presence and absence of employees at their workplace and their working needs. In case 5, a proposed LED lighting system is used that keeps the value of illumination of employees’ tables as low as possible based on the comfort zone of employees.

Figure 4 shows light intensity lux parameter response based on the set reference lux point with minimal error at steady state condition. In order to measure the light intensity in terms of lux value light meter with a range of 72 to 6693 Tenma is used. We keep the 2 second of operation cycle duration for controlling the LED system and it was observed that to reach a steady state condition the controlling time is about 10 minutes. Users preferred steady or gradual light intensity change rather than abruption and fast lighting response.

Fig. 4

Performance Analysis of Proposed WSN based Self-Tuning LED Lighting System.

From Fig. 4, it was observed that the highest error percentage value at steady condition state is approximately 1.63% which is calculated by using reference lux set value point and measured intensity of light. The lowest error percentage value is arising because of sensors, installed location of sensors and ambience intensity of light. From Fig. 4. It is also clearly seen that with the proposed LED system provides very little fluctuation and it is almost negligible that for the human eye it is impossible to sense or observe. Therefore the proposed system provides a stable and self- tuned LED lighting system that not only satisfied the user’s needs but also helpful in saving energy with minimum illumination value and maximum user comfort zone.

For analyzing the comfort zone of users, we use Kruithof curve as shown in Fig. 5 illustrate luminance area with color temperatures based on the consideration of comfortless and pleasing environment of user. Here we are using the Kruith of curve with 4,000 K of color temperature that is helpful in saving energy consumption with maximum extent but in minimum luminance value within the comfort level of the user.

Fig. 5

Kruithof curve with User’s comfort zone [38].

Based on the performance analysis of proposed System it was seen that proposed fitness function, offer 360 lux value as the most comfortable level of lighting that satisfies the user need and saves energy simultaneously. Energy consumption and with considerable energy saving in each case with the proposed system is shown in Table.

Table 1 shows the lighting power consumption in different scenarios with the proposed control strategy. The energy consumption of the lightening system without any control on brightness of room is 328 W.

Table 1

Energy Consumption of lighting system in different Cases

Sr. No.	Cases	Illumination value (lux)	Energy Consumption(W)	Energy Saving(W)	Saving in Percentage (%)
1	Most eye-relaxed and comfort	360	288.12	37.17	12.11%
2	Presence or absence of user	360(absence)	272.23	54.34	17.96%
3	User Preference	300	228.57	101.32	32.18%
4	Proposed Self-Tuned LED Lighting System	180	150.27	191.22	78.13%

The performance of lighting system in most eye-relaxed and comfort case is demonstrated with 288.12 W of energy consumption, 37.17 W of energy saving i.e around 12.11% of energy can be saved with 6.32 % of additional saving of energy can be attained by considering the movement of user. In user preference case, 228.57 W of energy is consumed and 101.32 W energy is saved. So in this case 32.18% of energy can be saved. In the proposed case with minimum illumination value and maximum comfort, only 150.27 W of energy is consumed with a saving percentage of 67.73% and 191.22 W of energy can be saved. The graphical representation of evaluation parameter measure is shown in Fig. 6. From the

Figure 6 it is clearly seen that with proposed self-tuned LED lighting system we achieve maximum energy saving with minimum consumption.

Fig. 6

Performance analysis of lightening system as function of energy consumption.

4 Conclusion

With constant utilizing of non-renewable energy resources, a time comes when all the resources get exhausted so in order to maintain the ecosystem it is necessary to start using renewable resources for energy consumption. With this aim, we had proposed wireless sensor network base self-tuned LED lighting system power by solar energy. In the proposed system based on the required ambient light intensity, we provide an energy efficient lighting system with a self-tuned control system based on the occupancy of the user in the room. To control the lighting system we use PIR and LDR sensors to gather user light requirements and their occupancy. The information from sensor transmitted to process controller using WSN communication medium for controlling LED system via DALI controller. Based on this information, the DALI controller tuned the LED system of the user’s occupancy room. We also sent this contextual information regarding the motion of the user, ambience light and temperature to cloud storage for real time monitoring and controlling. To extend this real-time monitoring and controlling self-tuned LED lighting system, the battery life with limited energy contribution makes a decision of operating time of the wireless communication sensor node. In that way, the minimal consumption of energy is needed in the wireless sensor node operation. The performance analysis of the proposed system is evaluated by based on the light intensity requirement and energy consumption. The proposed system offers 1.63 % steady state error percentage and 78.13% energy saving. With such energy saving percentage, operating life span of the wireless sensor nodes, and self-tuned LED lighting system is capable of optimizing intensity value of light that meet requirement and satisfaction criteria of every user. For more renewable energy, the proposed system can be extended by integrating solar energy with wind turbines so that it can handle power systems with LED lighting load in abnormal frequency controlled events.

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