Predictive Analytics of Energy Usage by IoT-Based Smart Home Appliances for Green Urban Development

Home Energy Management System (HEMS) is a technology to reduce and manage home energy use. The feedback on energy consumption to energy users is known to be effective to reduce total energy use. A typical HEMS just shows the energy consumption of the whole home and home appliances. Users cannot figure out how efficient a home appliance is, compared to the others. So it is necessary to compare the energy usage of home appliances to that of the same kinds of home appliances. In this paper, we propose a green HEMS based on energy comparison.

This paper studies how to optimize the energy usage of home appliances in the demand response framework from the consumer's perspective. The loads of major home appliances are divided into three categories: fixed loads, regulate-able loads, and deferrable loads. For efficient usage of the home appliances, an integrative optimization of the three category loads is needed. The paper investigates the relation of the integrative optimization and individual optimization of each category load. A regression-based learning strategy is employed to learn HAVC energy consumption model for development of more efficient DR policy. The study is conducted through an integrative computational experiment approach that combines a home energy simulator and MATLAB together for demand response development and evaluation. The paper examines how the integrative demand response of the residential home appliances are affected by dynamic pricing tariffs, seasons, and weather. Case studies are conducted by considering home energy consumption, dynamic electricity pricing schemes, and demand response methods.

Does smart city policy promote urban green and low-carbon development?

This paper presents methods for prediction of energy usage of different appliances in homes. Dataset comprising 14804 samples include measurements of weather from a nearby airport station, temperature and humidity sensors from a wireless network and recorded energy use of lighting fixtures. These measurements are sorted into 32 features, from which 17 were filtered and showed to be sufficient for energy usage prediction. Two methods for prediction were trained and tested: Random forest and Random tree. The performance of the methods was studied and it has been showed that the random forest gives better results than random tree method and that it has good performance in prediction of energy use of appliances.

Subway opening enables urban green development: Evidence from difference-in-differences and double dual machine learning methods.

Internet of things (IoT) is a developing concept, which aims to associate billions of devices with each other. The IoT devices sense, assemble, and transfer important information from their environments. This exchange of very large amount of information among billions of devices makes an enormous energy need. The radical growth in urbanization over the last few years needs sustainable, proficient, and smart clarifications for transport, governance, environment, quality of life, and so on. The IoT propose many urbane and universal applications for smart homes. The energy demand of IoT applications is greater than before; while IoT devices carry on to grow in both numbers and necessities. Therefore, IoT-based smart home and its automation must have the capability to competently consume energy and control the allied challenges. Energy management is considered as a key prototype for the comprehension of composite energy systems in smart homes. Further, smart home solutions have to be energy-efficient from both the users’ and environment’s points of view. In other words, smart home solutions have to be energy-efficient, cost-efficient, reliable, secure, and so on. For example, IoT devices should operate in a self-sufficient way without compromising quality of service (QoS) in order to enhance the performance with unremitting network operations. Therefore, the energy efficiency and life span of IoT devices are the key issues to next generation smart home solutions. It has been studied the electrical energy consumption from a prevailing house to make it further efficient presenting as much as possible IoT applications. The smart home applications that are straightforwardly associated with energy efficiency are obviously the light and the temperature monitoring. Hence, they are significant to assure the energy saving. Other smart home arrangements, similar to Fire Detection, Security, are not straightforwardly linked with the energy efficiency. Keywords: IoT, smart home, energy efficiency, Home Appliances, smart grid Cite this Article Partha Ghosh, Suradhuni Ghosh. IoT and Machine Learning in Green Smart Home Automation and Green Building Management. Journal of Alternate Energy Sources & Technologies . 2019; 10(3): 8–36p.

Full implementation of green urban planning and development has become the main theme of urban revival and development, but the construction and development of green infrastructure occupies a very important position in green urban development. Green infrastructure includes the micro-energy, high efficiency and low pollution engineering-form infrastructure and the urban green space network, which has at least the functions of improving urban environment and saving energy and reducing consumption. Factors restricting the planning and construction of green infrastructure have the changes in the philosophy of urban planning and design, green technological innovation and grant investment costs. However, because China is at the stage of urbanization and low carbon strategy development, there are still many opportunities. To accelerate the planning and construction of green infrastructure, it is necessary to take policies and measures on relevant standards and specifications, green assessments and audits, taxation and financial support and other aspects.

Urban green development (UGD) is a highly topical issue. To assess the degree of UGD, in this paper, we use the driving forces, pressures, states, impacts, and responses (DPSIR) model to evaluate UGD with a collection of 40 indicators based on the three aspects of resource depletion, environmental damage and ecological benefits. The established system of indicators is then applied to evaluate the UGD in Beijing from 2000 to 2014 as a case study. The results demonstrate that it is essential to analyze the trend in the change in resource depletion, which had a high weight of 0.556 because environmental damage and ecological benefits partly changed in response to this driving force and pressure. However, the UGD index value of environmental damage (positive index) has decreased since 2010. By ranking the degree of correlation among indicators, it can be seen that UGD is highly related to the lifestyle, status quo, technology and education, industrialization, environmental quality, and ecological environment of a city. The health situation in Beijing has improved in the past 15 years; it was determined to be very unhealthy (75% at the very unhealthy level (V) and 9% at the very healthy level (I)) in 2000 but very healthy (8% at the very unhealthy level (V) and 60% at the very healthy level (I)) in 2014. However, there are internal problems due to imbalanced development in Beijing related to aspects such as the ecological environment, population and economy, social life, investment management, energy consumption and urban infrastructure. And government should adjust the energy structure, formulate detailed plans and policies on urban environment, and increase investment in education and business development.

Digital economy has become an important engine for promoting green development.The existing literature mainly analyzed the impact of digital economy on green development, but rarely examines the coordinated development relationship between the two. This study contributes to the literature by constructing a research framework for the coupling coordination of digital economy and urban green development, using entropy method, coupling coordination model, and spatial auto-correlation to examine the coordinated development relationship between the two.The results show that:(1) During the research period, the comprehensive level of digital economy and urban green development in the Yangtze River Delta (YRD) both showed an upward and consistent trend. The level of digital economy was significantly lower than that of green development, but the gap between the two continued to narrow. (2) The overall coupling coordination level between digital economy and green development in the 41 cities of YRD was relatively low but showing a continuous upward trend. It has undergone a transition from near imbalance to primary coordination, still with a long way to realize high-quality coordination. (3) The coupling coordination level of digital economy and urban green development in different regions of YRD showed spatial differences, the order from high to low was Shanghai, Zhejiang, Jiangsu and Anhui province respectively. (4) The coupling coordination of digital economy and urban green development in the YRD exhibited significant spatial positive correlation and spatial dependence characteristics. The research results can provide a new perspective to promote the positive interaction between green development and digital economy.

The coordinated promotion of urban digitalization and green development is an inevitable requirement for sustainable development in the digital age. Based on the coupling mechanism of urban digitalization and green development, in this study, we took 282 cities at the prefecture level and above in China from 2011 to 2019 as the research object, and we constructed the evaluation index system and calculated the coupling coordination degree (CD&GDD) of the two through the coupling coordination degree model. We further used the Dagum Gini coefficient, kernel density estimation, Markov chain and Moran's I to assess the spatial effects of the regional differences, dynamic evolution trends and degree of coupling coordination. The results show the following: (1) The level of urban digitalization and green development show a fluctuating upward trend, and the interaction between the two is obvious. (2) Although the CD&GDD of most cities is continuously improving, it is still at a low level. There are large differences in the levels between the regions. (3) The inter-regional differences are the main source of the large overall differences in the CD&GDD in China, and these are mainly composed of the hypervariable density and net differences between the regions. (4) The phenomenon of "club convergence" exists in the CD&GDD. (5) The coupling coordination relationship between cities has a substantial spatial effect, and the spatial effect has obvious regional heterogeneity. The results and conclusions provide a reference for developing countries to promote green and low-carbon urban development.

The in-depth participation and application of new-generation information and communication technologies, such as big data, Internet of Things, artificial intelligence, etc., in the field of smart cities have promoted their abilities in urban fine governance, public services, ecological livability, scientific and technological innovation, etc. Smart cities are gradually becoming recognized as the best solution to “urban problems”. Smart city construction drives urban innovative development, accumulates kinetic energy for economic growth, strengthens social support functions, enhances the effectiveness of the ecological environment, and promotes the convergence and integration of urban green development and high-quality development. This paper constructs a difference-in-differences model based on propensity score matching. Additionally, fiscal science and technology investment is introduced as mediating variables to further explain the mechanism through which smart city pilot policy impacts urban green and high-quality development. This research uses panel data from 156 prefecture-level cities in China from 2006 to 2019 to empirically test that the construction of smart cities has a significant positive effect on urban green and high-quality development. The mediation effect model shows that an increase in the level of local government’s fiscal science and technology investment enhances the positive effect of smart city construction on urban green and high-quality development. This research concludes with policy recommendations: the government should seize the development opportunity presented by smart city pilot policy, providing necessary policy support and financial incentive for the construction of smart cities. This will optimize the local economic structure, transform the driving forces of urban development, and assist cities in achieving green and high-quality development.

This paper focuses on the development of a smart energy meter that can monitor the energy usage of different appliances. A smart energy meter is a digital electric meter that measures the electricity generation, consumption and provides other additional features such as advanced billing system and high accuracy which makes it more advantageous than the traditional energy meter. The proposed smart meter model is verified by designing an appropriate circuit and associated hardware. The hardware is designed using a microcontroller PIC16F877, current and voltage transformer, voltage regulator 7805, solar panel, solar charge controller and inverter. The developed energy meter can control the energy supply and usage of the consumers accurately based on load requirement. In addition, the meter can calculate the cost of power consumption of convection grid and solar energy. Thus, the consumer will get a clear idea about the costs of their usage. Hence, the proposed metering system is more advantageous than the traditional metering system which will reduce the manpower, cost and time.

As a high-quality and sustainable growth model, green development has different economic, ecological, and social dimensions and is strategically important for the realization of modern city construction and the sustainable development of human society. The low-carbon city pilot policy (LCCP) is an innovative initiative for promoting green urban development and building a harmonious society in China. Based on balanced panel data from 277 prefecture-level cities from 2007 to 2020, this paper measures the level of urban green development in terms of three dimensions: green economic growth, ecological welfare enhancement, and social welfare increase. This paper also adopts a multi-period difference-in-differences (DID) method for investigating the impact of LCCP on green development with the panel dataset. The results of the study show that: (1) LCCP is generally beneficial to urban green development, and the results still hold after a series of robustness check analyses. (2) The results of the mechanism analysis show that the construction of low-carbon cities has improved the level of green technology innovation, thereby promoting the level of regional green development. Environmental regulation has a masking effect between low-carbon city construction and green development in this study. When environmental regulation is controlled for, the coefficient of the effect of LCCP on green development increases, reflecting that environmental regulation also plays an important role between the two. (3) According to the geographical location, whether it is a resource-based city, and the city cluster, we found that the low-carbon city pilot policy has a significant positive role in promoting green development in the central region, non-resource-based cities, and the Jing-Jin-Ji, but not in the eastern region, the western region, the Yangtze River Delta and Pearl River Delta. We also found that in resource-based cities, this effect presents a significant negative relationship. The above findings enrich the literature on low-carbon city pilot policies and green development and provide Empirical evidence for relevant countries and regions to carry out low-carbon city pilots.

The Internet of Things (IoT) represents the concept of cognitive networked devices that measure their environment and act on it intelligently. For instance, health sensors monitor vital human signs and inform their owner; smart meters measure the energy consumption and relay the information in real time to energy providers and consumers; and smart thermostats optimize heating while reducing costs. Though most IoT devices are designed to work alone, collective operation advances their capabilities. In a smart building application, for instance, several devices from temperature and presence sensors to heating and lighting appliances, cooperate to maximize energy efficiency and comfort. From the application perspective, presence sensors feed lighting and heating appliances with information; from the networking perspective, all these sensors and actuators relay each other's traffic for connectivity (if the medium is wireless). Without cooperation context awareness fails and wireless multi-hop networks collapse. Unfortunately, when the altruistic act of cooperation is costly, devices become selfish. For a battery-powered device, forwarding a neighbor's packet increases its energy consumption and consequently, decreases its lifetime. Therefore, that device does not cooperate and refrains from forwarding foreign packets. When all nodes in a wireless network follow the same reasoning, none of the packets are relayed, and consequently the network gets disconnected. In this thesis, first, we investigate the mechanisms and incentives for cooperation and reveal that social relations such as family and friendship are crucial. Then, we automate cooperation mechanism for devices based on social relations. Advancing ``smart'' IoT devices by making them ``social'' is becoming a hot topic in IoT research. It is argued that social devices can share their data and assist each other without requiring human intervention and consequently, improve their management. But, what is the meaning of a social device? Being a social device does not necessarily mean assisting all others by sharing data and forwarding packets. A social device has its own identity and social profile such that it is aware of its owner. The criterion of assisting others is its owner's preferences, which are embedded in social relations. As we prove in the thesis, consumers desire to know to whom they assist, suggesting that peers should be inside the circle of trusted social relations. Social relations are crucial for cooperation, now the question is: how can we automate cooperation decisions based on social relations? Without automation, consumers cannot manage all their devices' interactions. The reason is that IoT imposes the challenge of scaling up to billions of devices such that each person will be equipped with tens of devices. Our solution is a decentralized architecture where every device is identified by a URI that points to the social profile of that device. Ownership relations are declared in this social profile. When a resource server (e.g., light bulb, temperature sensor) receives a request from a client device (e.g., smartphone), the resource server crawls the client's and its owner's social profile. If the resource server discovers a social relation that grants access, it responds positively to the client's request. Unlike centralized approaches, our decentralized proposal protects privacy, provides end-to-end security, and can operate without an Internet connection. The drawback of our approach is the complexity of searching decentralized social profiles especially for indirect relations such as friend-of-a-friend. For unconstrained devices, we limit the search space based on proximity. In an access point (AP) scenario, the AP overhears WiFi beaconing messages from clients to discover their existence. For constrained devices, the whole search operation is delegated to a more resourceful cloud service. Our solutions for social network integration depend on secured identity information. Unfortunately, highly constrained devices that have less than 20~KBs of memory cannot be protected from identity-related attacks. These constrained devices can neither punish their defector neighbors nor reward only cooperators. They either cooperate always and are exploited by free-riders or defect always and disrupt network traffic. In this thesis, we offer adaptability to these devices via meta-strategies that only require local information. Devices overhear the traffic in their neighborhood and practice the best local strategy (defection or cooperation). We show that even if free-riders change their identities, meta-strategies protect them against exploitation while still promoting cooperation throughout the network. All in all, in this thesis we make a few stepts towards the goal of autonomous cooperation in IoT; and in particular we show that 1. social relations are crucial in cooperation decisions, 2. decentralized social-device networks (proposed in this thesis) can automate cooperation and provide secure-by-default IoT systems, 3. constrained devices that are vulnerable to identity-change attacks can protect themselves by observing the traffic in their neighborhood.

In recent years, the Republic of Belarus has developed the practice of introducing elements of green infrastructure into urban development programs. This is a contribution to the transition to environmentally friendly production technologies, the construction of buildings with a low share of energy and resource consumption, the implementation of environmental-oriented transport infrastructure, the use of effective technologies for the collection, disposal and processing of waste, and an increase in amount of green areas in cities. From 2016 to the present, the country is implementing a 5-year project of the United Nations Development Programme (UNDP) “Supporting Green Urban Development in Small and Medium-Sized Cities in Belarus”. The objective of the Project is the growth of development of green urban development plans and pilot green urban development initiatives in the cities of Polack, Navapolack, Navahrudak related to energy efficiency and sustainable transport. An important component in the formation of green urban planning is the operation of spatial information. For this purpose, mapping and geoinformation approaches were applied in the study. They made it possible to identify the modern features of the distribution of green city indicators in pilot cities, carry out their analysis and propose a new development strategy that will improve the blue-green infrastructure. For each city, in the instrumental geographic information systems ArcGIS and QGIS, a methodology was developed and indicators were mapped that characterize condition of residential areas, quality of buildings, population density, location of green areas, proximity of public transport stops and other urban infrastructure, tourism service infrastructure and the distribution of energy users and sources of CO2 emissions. Based on the results of GIS analysis of the obtained layers of indicators of the profile of the green city, a spatial development strategy was formed. The information of the thematic layers on indicators of urban development became the basis for the creation of a series of web maps in ArcGIS Online, which are currently being discussed by residents of key cities. The cartographic materials prepared within the project can be finalized and used to work with city administrations, as well as to inform the population about the state of the city and the decisions taken.