دانلود مقاله ISI انگلیسی شماره 46264
ترجمه فارسی عنوان مقاله

کاهش بار مسکونی هوشمند از طریق منطق فازی، سنسور بی سیم، و انگیزه های شبکه هوشمند

عنوان انگلیسی
Smart residential load reduction via fuzzy logic, wireless sensors, and smart grid incentives
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
46264 2015 16 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Energy and Buildings, Volume 104, 1 October 2015, Pages 165–180

ترجمه کلمات کلیدی
منطق فازی - سنسور بی سیم - انگیزه های شبکه هوشمند - کاهش بار - مدیریت سمت تقاضا - ساختمانهای مسکونی - سیستم های تهویه مطبوع - ترموستات
کلمات کلیدی انگلیسی
Fuzzy logic; Wireless sensors; Smart grid incentives; Load reduction; Demand-side management; Residential buildings; HVAC systems; Thermostats
پیش نمایش مقاله
پیش نمایش مقاله  کاهش بار مسکونی هوشمند از طریق منطق فازی، سنسور بی سیم، و انگیزه های شبکه هوشمند

چکیده انگلیسی

The incentives such as demand response (DR) programs, time-of-use (TOU) and real-time pricing (RTP) are applied by utilities to encourage customers to reduce their load during peak load hours. However, it is usually a hassle for residential customers to manually respond to prices that vary over time. In this paper, a fuzzy logic approach (FLA) utilizing wireless sensors and smart grid incentives for load reduction in residential HVAC systems is presented. Programmable communicating thermostats (PCTs) are used to control residential HVAC systems in order to manage and reduce energy use, while consumers accommodate their everyday schedules. Hence, the FLA is embedded into existing PCTs to augment more intelligence to them for load reduction, while maintaining thermal comfort. To emulate an actual thermostat, a PCT capable of handling both TOU and RTP is simulated in Matlab/GUI. It is utilized as a ‘simulator engine’ to evaluate the performance of FLA via applying several different scenarios. The results show that the FLA decreases/increases the initialized set points without jeopardizing thermal comfort by applying specific fuzzy rules through evaluating the information received from wireless sensors and smart grid incentives. Our approach results in better energy and cost saving in residential buildings versus existing PCT.