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The use of energy storage devices helps the consumers to utilize the benefits and flexibilities brought by smart networks. One of the major energy storage solutions is using electric vehicle batteries. The purpose of this paper is to develop an optimal energy management strategy for a consumer connected to the power grid equipped with Vehicle-to-Home (V2H) power supply and renewable power generation unit (PV).

The problem of energy flow management is formulated and solved as an optimization problem using a linear programming model. The total energy cost of the consumer is optimized. The optimal values of decision variables are found using CPLEX solver.

The simulation results demonstrated that if the optimal decisions are made regarding the V2H operation and managing the produced power by solar panels then the total energy payments are significantly reduced.

The gap that the proposed model is trying to fill is the holistic determination of an optimal energy procurement portfolio by using various embedded resources in an optimal way. The contributions of this paper are in threefold as: first, the introduction of mobile storage devices with a periodical availability depending on driving schedules; second, offering a new business model for managing the generation of PV modules by considering the possibility of grid injection or self-consumption; third, considering Real Time Pricing in the suggested formulation.

The paper aims to provide a comprehensive analysis of artificial intelligence’s (AI) transformative impact on the real estate industry. By examining various AI applications, from property recommendations to compliance automation, this study highlights potential benefits such as increased accuracy and efficiency. At the same time, this study critically discusses potential drawbacks, like privacy concerns and job displacement. The paper's goal is to offer valuable insights to industry professionals and policy makers, aiding strategic decision-making as AI continues to reshape the landscape of the real estate sector.

This paper employs an extensive literature review, combined with a qualitative analysis of case studies. Various AI applications in the real estate industry are examined, including machine learning for property recommendations and valuation, VR/AR property tours, AI automation for contract and regulatory compliance, and chatbots for customer service. The study also delves into the optimisation potential of AI in building management, lead generation, and risk assessment, whilst critically discussing potential challenges such as data privacy, algorithmic bias, and job displacement. The outcomes aim to inform strategic decisions for industry professionals and policy makers.

The study finds that AI has significant potential to revolutionise the real estate industry through enhanced accuracy in property valuation, efficient automation and immersive AR/VR experiences. AI-driven chatbots and optimisation in building management also hold promise. However, this study also uncovers potential challenges, including data privacy issues, algorithmic biases, and possible job displacement due to increased automation. The insights gleaned from this study underscore the importance of strategic decision-making in harnessing the benefits of AI while mitigating potential drawbacks in the real estate sector.

The paper's practical implications extend to industry professionals, policy makers, and technology developers. Professionals gain insights into how AI can enhance efficiency and accuracy in the real estate sector, guiding strategic decision-making. For policy makers, understanding potential challenges like data privacy and job displacement informs regulatory measures. Technology developers can also benefit from understanding the sector-specific applications and concerns raised. Additionally, highlighting the need for addressing algorithmic bias and privacy concerns in AI systems may foster better design practices. Therefore, the paper's findings could significantly shape the future trajectory of AI integration in real estate.

The paper provides original value by offering a comprehensive analysis of the transformative impact of AI in the real estate industry. Its multi-faceted examination of AI applications, coupled with a critical discussion on potential challenges, provides a balanced perspective. The paper's focus on informing strategic decisions for professionals and policy makers makes it a valuable resource. Moreover, by considering both benefits and drawbacks, this study contributes to the discourse on AI's broader societal implications. In the context of rapid technological change, such comprehensive studies are rare, adding to the paper's originality.

The concept of sustainable performance is gaining attention from researchers all over the world for the betterment of society as a whole. Pollution and climate change has instigated an awareness to switch to sustainable built environment. The present research aims to analyze the indicators of adoption of smart buildings and their impact on dimensions of sustainability, namely, economic, social and environmental performance.

Data was collected from 332 respondents staying in smart buildings. Structural equation modeling was applied to analyze the data.

The results of the study indicated adoption of smart buildings depicted a stronger perceived sustainable environmental and economic performance while social performance emerged as a weak outcome variable as compared to the other two performances.

The study thus has tremendous implications for construction companies so that they may design smart buildings by adequately using artificial intelligence. To the best of the authors’ knowledge, this research is one of the initial studies to understand the perception of residents of smart buildings using smart technology in India.

The need to address energy management as a significant innovation in the smart grid is emphasized to enable a more effective penetration of renewable energy to achieve energy savings and CO₂ emission reductions. The purpose of this study is to propose a holistic, flexible decision framework for energy management in a smart grid.

According to the situation actor process−learning action performance (SAP−LAP) model, the variables have been identified after a comprehensive analysis of the literature and consideration of the opinions of domain experts. However, the importance of each SAP−LAP variable is not the same in real practice. Hence, focus on these variables should be given based on their importance, and to measure this importance, an interpretive ranking process based ranking method is used in this study. This helps to allocate proportionate resource to each SAP−LAP variable to make a better decision for the energy management of the smart grid.

This study ranked five actors based on their priorities for energy management in a smart grid: top management, generator and retailor, consumers, government policy and regulation and technology vendors. Furthermore, actions are also prioritized with respect to performance.

The SAP−LAP model conveys information about the state of energy management in India to actors who may proceed or manage the flow of electricity. Additionally, this study aids in detecting vulnerabilities in the current energy generation, transmission and distribution technique. The synthesis of SAP results in LAP, which assists in recommending improvement actions learned from the current situation, actors and processes.

The SAP−LAP model is a revolutionary approach for examining the current state of energy management in a unified framework that can guide decision-making in conflicting situations, significantly the contradictory nature of India’s renewable energy and power sectors.

Using a unique data set, the purpose of this paper is to test the hypothesis that tenants pay increased accommodation costs for space in energy efficient office property.

The authors obtain lease contracts for office space in central Sydney, Australia. Empirical data on annual gross face rent and contract terms from each lease are combined with building characteristics and measured energy performance at the time of lease. Hedonic regression isolates the effect of energy performance on gross face rent.

No significant price differentials emerged as a function of energy performance, leading to a conclusion that tenants are not willing to pay for energy efficiency. Six factors – tenancy floor level, submarket location, proximity to transit, market fixed effects, building quality specification and, surprisingly, outgoings liability – consistently explain over 85 per cent of gross face rent prices in Sydney.

Rent premiums from an asset owner's perspective could emerge as a result of occupancy premiums, market timing or agent bias combined with statistically insignificant rental price differentials.

Tenants are likely indifferent to energy costs because the paper demonstrates that energy efficiency lacks financial salience and legal obligation in Sydney. This means that split incentives between owner and tenant are not a substantial barrier to energy efficiency investment in this market.

This study is the first to thoroughly examine energy efficiency rent price premiums at the tenancy scale in response to disclosure of measured performance. It also presents evidence against the common assumption that rent premiums at the asset scale reflect tenant willingness to pay for energy efficiency.

The purpose of this paper is to introduce a problematic phenomenon that can occur when managing multi electrical sources systems by optimization.

The energy management problem is formulated as a linear optimisation problem. Two approaches are developed and applied to detect the possible existence of equivalents solutions. The first is based on Dulmage‐Mendelsohn (DM) decomposition. With this method the structure of the optimisation problem is analysed. The second approach is a numeric approach; the detection of equivalents solutions is made by the formulation of new optimisation problem and the objective function of this problem is to maximise the distance between two equivalents solutions.

The numeric approach is more efficient than the structural approach. In some cases, applying DM decomposition may not be sufficient to detect the risk of W effect. This is because DM decomposition does not take the value of variable's coefficient into consideration, which is important to determine the degrees of freedom in the set of variables.

Multi sources systems are widely used, especially in buildings where renewable energies have good potential application. The linear formulation of the management problem may induce an existence of equivalent command strategies. The detection approach presented in this paper shows that some solutions are better than others from an applicabability point of view. They will not exhaust rapidly the storage system. This approach can be implemented in virtual sources plant to avoid solutions with this so‐called W effect.

This paper aims to study a power management circuit for a piezoelectric vibration energy harvester. It presents how to accumulate energy and provide regulated DC voltage for practical applications.

Energy storage and extraction circuit are proposed. While the storage stage consists of a full wave rectifier and a storage capacitor, the extraction stage includes a voltage comparator and regulator, which may provide the load steady DC voltage when the voltage of the storage capacitor is higher than the threshold.

The numerical analysis and experimental results indicate that it takes a longer time to charge to a specified voltage for the greater storage capacitor and the net charge flowing into the storage capacitor during each period decreases when the voltage of the storage capacitor is higher. The higher threshold voltage of the capacitor has lower harvesting efficiency owing to the rate of charging of the storage capacitor slowing down over time.

Because of the chosen research method, the power management circuit is only suitable for the piezoelectric vibration energy harvester under resonant conditions.

This study includes practically useful applications for users to build a power management circuit for piezoelectric energy harvester.

This study presents results that the charging efficiency of the storage circuit is relative to the storage capacitor and the threshold voltage.

Steady progress has been achieved with implementing legislated public sector equal employment opportunities (EEO) policies and programs to fulfil the aim of increasing the representation of EEO groups in public employment. However, there remain areas of significant gender differences in the public sector labour market. One of the most persistent problems has been segregation by gender and this characteristic employment pattern can be discerned across industry, occupation, firm and type of employment contract. This research compares and contrasts the effect of gender domination on the gender composition of tiers of management and numbers in the Senior Executive Service across a state government public service in Australia. The career progression of males and females in female‐dominated and male‐dominated agencies are examined to determine whether different career outcomes can be discerned under conditions of significantly greater numbers of one gender being employed. Findings indicate that men in female‐dominated agencies have different employment profiles and career patterns to those of women in male‐dominated agencies.

The purpose of this article is to build a new frame of reference for exploring the value of hierarchy as a learning platform as organizations move away from bureaucracies and toward complex adaptive behavior.

Observations of the behaviors of horses and the hierarchical structure within which they function are introduced to explore the value of hierarchy as a learning platform. The concepts of bureaucracy and hierarchy are juxtaposed based on a literature review. Moving to individual learning, functioning space is introduced and then extrapolated across to organizations. Finally, these interwoven ideas are used to pose questions.

The paper posits that recognition of the distinction between hierarchy and bureaucracy places us in a framework to reap the benefits of hierarchy in our thinking, talking and acting as our organizations move toward complex adaptive behavior.

This paper discusses important distinctions between hierarchy and bureaucracy in support of a learning framework. It also introduces the relationship of thinking, talking and acting to our functioning space.