A new optimized demand management system for smart grid-based residential buildings adopting renewable and storage energies

Demand Side Management (DSM) implies intelligently managing load appliances in a Smart Grid (SG). DSM programs help customers save money by reducing their electricity bills, minimizing the utility’s peak demand, and improving load factor. To achieve these goals, this paper proposes a new load shifting-based optimal DSM model for scheduling residential users’ appliances. The proposed system effectively handles the challenges raised in the literature regarding the absence of using recent, easy, and more robust optimization techniques, a comparison procedure with well-established ones, using Renewable Energy Resources (RERs), Renewable Energy Storage (RES), and adopting consumer comfort. This system uses recent algorithms called Virulence Optimization Algorithm (VOA) and Earth Worm Optimization Algorithm (EWOA) for optimally shifting the time slots of shiftable appliances. The system adopts RERs, RES, as well as utility grid energy for supplying load appliances. This system takes into account user preferences, timing factors for each appliance, and a pricing signal for relocating shiftable appliances to flatten the energy demand profile. In order to figure out how much electricity users will have to pay, a Time Of Use (TOU) dynamic pricing scheme has been used. Using MATLAB simulation environment, we have made effectiveness-based comparisons of the adopted optimization algorithms with the well-established meta-heuristics and evolutionary algorithms (Genetic Algorithm (GA), Cuckoo Search Optimization (CSO), and Binary Particle Swarm Optimization (BPSO) in order to determine the most efficient one. Without adopting RES, the results indicate that VOA outperforms the other algorithms. The VOA enables 59% minimization in Peak-to-Average Ratio (PAR) of consumption energy and is more robust than other competitors. By incorporating RES, the EWOA, alongside the VOA, provides less deviation and a lower PAR. The VOA saves 76.19% of PAR, and the EWOA saves 73.8%, followed by the BPSO, GA, and CSO, respectively. The electricity consumption using VOA and EWOA-based DSM cost 217 and 210 USD cents, respectively, whereas non-scheduled consumption costs 273 USD cents and scheduling based on BPSO, GA, and CSO costs 219, 220, and 222 USD cents.publishedVersio

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

Desain dan Implementasi Smart Home Konsumsi Daya Rendah Menggunakan Algoritma Optimisasi Cuckoo-Earthworm

Penggunaan energi merupakan hal yang paling penting pada sistem smart home, karena dengan energi yang kecil maka sistem smart home akan semakin efisien dan juga ekonomis. Konsumsi energi pada sistem smart home dioptimalkan dengan teknik penjadwalan peralatan secara real time berdasarkan harga listrik pada saat itu dan persentase kenyamanan pengguna dimana konsumsi daya setiap peralatan diatur untuk melakukan penghematan. Melalui pendekatan ini pengguna dapat menentukan tingkat kenyamanan secara fleksibel untuk melakukan penghematan tanpa mengurangi kenyamanan yang diperoleh dari setiap peralatan rumah tangga. Algoritma Cuckoo-Earthworm digunakan untuk proses penjadwalan peralatan secara real time. Sistem smart home dilengkapi dengan Raspberry Pi3 sebagai HUB controller dan Smart Plug yang digunakan untuk monitor energi yang digunakan pada setiap peralatan dan juga sebagai switch untuk melakukan penjadwalan. Komunikasi antara HUB controller dan setiap device menggunakan jaringan Z-Wave. Untuk user interface menggunakan Home Assistant. Pada implemetasi sistem smart home dengan daya rendah menggunakan algoritma Cuckoo-Eartworm kali ini didapatkan pengurangan biaya mencapai 41.39% dan energi mencapai 32.52% dari peralatan yang tidak terjadwal pada tingkat kenyamanan terendah

The GHG emission reduction toolkit : a case study of Blacktown City, Australia

This PhD thesis is in line with Australia’s national policy of a 26-28% reduction in its greenhouse gas emissions to 2005 levels. According to a review of its climate change policy in 2017, the Australian Government is committed to tackling climate change, while maintaining a strong economy, providing affordable energy and security for industries. This requires new initiatives in existing technologies to reduce greenhouse gas emissions or the emergence of new technologies altogether. Whatever the strategy, the final goal is to mitigate greenhouse gas emissions. This national target is now disseminated among different sectors and governmental bodies in Australia, requesting them to submit their action plans against climate change. This includes all Australian City Councils and incorporates Blacktown City Council as the Case Study for this study. As part of the Blacktown City Council’s commitment to reduce greenhouse gas emissions, this research study is the result of collaboration between the Council and Western Sydney University. The authorities of both sides have signed a research collaboration agreement, ample evidence of a local university tackling local problems. This research agreement is unique as it opens a door for other local Councils to collaborate with universities. Blacktown City Council, on the other side of this agreement, can improve its body of knowledge through a comprehensive investigation of greenhouse gas mitigation using its available tools. Therefore, this research study developed a toolkit to help reduce the Council’s GHG Emission

ME-EM 2013-14 Annual Report

Table of Contents Human-Centered Engineering Enrollment & Degrees Graduates Faculty & Staff Alumni Donors Contracts & Grants Patents & Publicationshttps://digitalcommons.mtu.edu/mechanical-annualreports/1005/thumbnail.jp

11th EEEIC International Conference on Environment and Electrical Engineering – Student Edition : Wrocław-Ostrava-Cottbus 7th - 12th of May 2012

In the time of increased awareness about the environment problems by the public opinion and also intensive international efforts to reduce emissions of greenhouse gases, as well increase of the generation of electrical energy to facilitate industrial growth, the conference offers broad contribution towards achieving the goals of diversification and sustainable development. Focus of the student conference is to promote the discussion of views from scientists and students from Wroclaw University of Technology, Technical University of Ostrava and Brandenburg Technical University of Cottbus. The conference offers prominent academics and industrial practitioners from all over the world the forum for discussion about the future of electrical energy and environmental issues and presents a base for identifying directions for continuation of research

ME-EM 2015-16 Annual Report

Table of Contents Alumni: Leading with Simulation Education: Simulating the Future Faculty: Advancing Simulation Graduate Seminar Series Enrollment & Degrees Graduates Department News Faculty & Staff Alumni Donors Contracts & Grants Patents & Publicationshttps://digitalcommons.mtu.edu/mechanical-annualreports/1003/thumbnail.jp

Optimization of energy consumption in smart homes using firefly algorithm and deep neural networks

Electronic gadget advancements have increased the demand for IoT-based smart homes as the number of connected devices grows rapidly. The most prevalent connected electronic devices are smart environments in houses, grids, structures, and metropolises. Smart grid technology advancements have enabled smart structures to cover every nanosecond of energy use. The problem with smart, intelligent operations is that they use a lot more energy than traditional ones. Because of the growing growth of smart cities and houses, there is an increasing demand for efficient resource management. Energy is a valuable resource with a high unit cost. Consequently, authors are endeavoring to decrease energy usage, specifically in smart urban areas, while simultaneously ensuring a consistent terrain. The objective of this study is to enhance energy efficiency in intelligent buildings for both homes and businesses. For the comfort indicator ("thermal, visual, and air quality"), three parameters are used: temperature, illumination, and CO2. A hybrid rule-based Deep Neural Network (DNN) and Fire Fly (FF) algorithm are used to read the sensor parameters and to operate the comfort indication, as well as optimize energy consumption, respectively. The anticipated user attributes contributed to the system's enhanced performance in terms of the ease of use of the smart system and its energy usage. When compared to traditional approaches in expressions of Multi View with 98.23%, convolutional neural network (CNN) with 99.17%, and traffic automatic vehicle (AV) with 98.14%, the activities of the contributed approach are negligibly commanding

Data-Intensive Computing in Smart Microgrids

Microgrids have recently emerged as the building block of a smart grid, combining distributed renewable energy sources, energy storage devices, and load management in order to improve power system reliability, enhance sustainable development, and reduce carbon emissions. At the same time, rapid advancements in sensor and metering technologies, wireless and network communication, as well as cloud and fog computing are leading to the collection and accumulation of large amounts of data (e.g., device status data, energy generation data, consumption data). The application of big data analysis techniques (e.g., forecasting, classification, clustering) on such data can optimize the power generation and operation in real time by accurately predicting electricity demands, discovering electricity consumption patterns, and developing dynamic pricing mechanisms. An efficient and intelligent analysis of the data will enable smart microgrids to detect and recover from failures quickly, respond to electricity demand swiftly, supply more reliable and economical energy, and enable customers to have more control over their energy use. Overall, data-intensive analytics can provide effective and efficient decision support for all of the producers, operators, customers, and regulators in smart microgrids, in order to achieve holistic smart energy management, including energy generation, transmission, distribution, and demand-side management. This book contains an assortment of relevant novel research contributions that provide real-world applications of data-intensive analytics in smart grids and contribute to the dissemination of new ideas in this area