Internet of things-based smart residential building energy management system for a grid-connected solar photovoltaic-powered DC residential building

State-of-the-art solutions of blockchain technology for data dissemination in smart cities: A comprehensive review

Smart cities are an innovative concept for managing metropolitan areas to increase their residents’ sustainability and quality of life. This article examines the management and evolution of energy generation, various storage systems and the applications they serve, and infrastructure technology’s current condition and future prospects. Additionally, the study also examines energy-related construction and transportation systems and technologies. The Smart Cities Energy Prediction Task Force predicts electrical usage using STLF, SVM, and e-learning machines. To keep a system working well throughout the year, fossil fuels must be utilised as a backup energy source. Technologies can only benefit if integrated into the city’s infrastructure. By 2050, it is anticipated that the global population will surpass 10 billion, with most people settling in metropolitan regions. Between 2020 and 2027, the global market for smart energy is anticipated to expand by 27.1% annually, from USD 122.2 billion in 2020 to USD 652 billion in 2026. In 2020, Europe will account for 31.8 per cent of total smart energy product sales. China’s GDP is projected to grow by 33.0 per cent annually, reaching USD 176.1 billion by the conclusion of the analysis period. Consequently, smart cities are expanding and blooming worldwide, yet there are no permanent standards.

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Energy and Sustainable Development in Smart Cities: An Overview

The rising cost and demand for energy have prompted the need to devise innovative methods for energy monitoring, control, and conservation. In addition, statistics show that 20% of energy losses are due to the mismanagement of energy. Therefore, the utilization of energy management can make a substantial contribution to reducing the unnecessary usage of energy consumption. In line with that, the intelligent control and optimization of energy management systems integrated with renewable energy resources and energy storage systems are required to increase building energy efficiency while considering the reduction in the cost of energy bills, dependability of the grid, and mitigating carbon emissions. Even though a variety of optimization and control tactics are being utilized to reduce energy consumption in buildings nowadays, several issues remain unsolved. Therefore, this paper presents a critical review of energy management in commercial buildings and a comparative discussion to improve building energy efficiency using both active and passive solutions, which could lead to net-zero energy buildings. This work also explores different optimum energy management controller objectives and constraints concerning user comfort, energy policy, data privacy, and security. In addition, the review depicts prospective future trends and issues for developing an effective building energy management system, which may play an unavoidable part in fulfilling the United Nations Sustainable Development Goals.

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A Review on Optimal Energy Management in Commercial Buildings

This article presents an optimal demand response (DR) control for a residential photovoltaic (PV) storage system using energy pricing limits. Unlike the existing literature, both energy buying and selling prices are considered in DR application for reducing the energy cost of the system. A method for determining buying price and selling price limits of the day using day-ahead predictions of energy price, load demand, and PV power profiles is proposed. To further reduce the energy cost of the system subbuying price limit is considered. A rule-based DR control for minimizing the energy cost of the system is proposed. The rules are formulated such that energy buying price and selling prices are limited to their corresponding price limits of the day with the flexible day-to-day management of battery energy storage system. For obtaining optimal results with rule-based control, required control inputs are determined optimally using genetic algorithm. The proposed method is tested in MATLAB. It is observed that energy cost savings of 34.09% and 5.4% are obtained on the day of more PV energy availability and less PV energy availability, respectively, based on the day-ahead operation. Further, the impact of uncertainties on the proposed DR control is discussed for real-time operation of the system. 

Optimal Demand Response in a Residential PV Storage System Using Energy Pricing Limits

Smart Energy Control Systems (SECS) has been increasingly implemented in the Smart homes scenario, due to the possibility of conditioning and controlling residential energy consumption, thus contributing to reducing energy losses and unnecessary electrical consumption. With the evolution of embedded systems in conjunction with the Internet of Things (IoT), Smart Outlet (SO), and devices that promote the Users Indoor Identification (UII) environments, they have assumed fundamental roles in the acquisition of data from electrical devices and also in the mapping of the individualized consumption of each resident throughout the day, providing essential information for SECS Systems as a way to assist in energy balance with minimal impact on the daily usability of electrical equipment. However, in most of the works that propose these types of assistance to SECS based on SO and UII, they have a massive implementation of sensors throughout the residence, misinterpretation of the data generated by the residents, difficulty in the identification of multiple residents. Thus, the present work proposes an evolution of an SECS architecture called SmartCom, with the implementation of accurate identification of electrical equipment through Near Field Communication (NFC)-based SO (data transfer between the appliance and the SO) and of multiple inhabitants through Wi-Fi handover using smartphones, with the least possible impact on the user’s comfort, as well as in the building structure, achieving a rebalanced residential energy consumption 87.3% of the time it was used.

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Gustavo O. Contente

Papers

A Smart Home Architecture for Smart Energy Consumption in a Residence With Multiple Users