کاهش مصرف انرژی و بهبود آسایش حرارتی و بصری در خانهها با سناریوهای کنترل سایهبان: تحلیل پارامتریک و بهینهسازی با یادگیری ماشین در بندرعباس، یزد، ساری و تبریز | ||
| صفه | ||
| دوره 35، شماره 3 - شماره پیاپی 110، 1404، صفحه 25-54 اصل مقاله (4.62 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.48308/soffeh.2025.233623.1300 | ||
| نویسندگان | ||
| فاطمه اخلاقی نژاد1؛ هادی باقری سبزوار* 2 | ||
| 1دانشجوی دکتری، دانشکدۀ معماری و شهرسازی، دانشگاه شهید بهشتی | ||
| 2استادیار دانشکدۀ معماری و شهرسازی، دانشگاه حکیم سبزواری، سبزوار، ایران | ||
| چکیده | ||
| اهداف و پیشینه: پیشبینی میشود که با افزایش چشمگیر مصرف انرژی در بخش ساختمانها تا سال 2040، بهینهسازی مصرف انرژی در این بخشْ یکی از اهداف اصلی در زمینۀ توسعۀ پایدار گردد. در این زمینه، طراحی سیستمهای سایهبان، که ابزاری است برای کنترل تابش خورشیدی و کاهش بارهای سرمایشی و گرمایشی، نقشی حیاتی در ارتقای کارایی انرژی دارد. هدف اصلی در این مطالعه، شناسایی استراتژیهای کنترلی بهینۀ سایهبانها با در نظر گرفتن عوامل مختلف مانند موقعیت سایهبان، جهتگیری ساختمان، و شرایط اقلیمی است. در این تحقیق، با بهرهگیری از نرمافزارهای انرژی پلاس و جیپلاس، سناریوهای مختلف طراحی سایهبان بهصورت پارامتریک در چهار شهر با اقلیمهای متفاوت (بندرعباس، یزد، ساری، و تبریز) مدلسازی و بهینهسازی شدهاند. هدف نهایی در این پژوهش، یافتن سناریویی برای کاهش مصرف انرژی و ارتقای آسایش حرارتی و بصری کاربران است و تمرکز اصلی بر ارزیابی تأثیر انتخاب استراتژی کنترلی مناسب سایهبانها در دستیابی به اهداف است. مواد و روشها: در این تحقیق از مدل استاندارد اتاق مسکونی مشابه نمونۀ 600 مطابق با استاندارد ANSI/ASHRAE 140-2017 استفاده شده است. در این مطالعه، 19 سناریوی مختلف برای کنترل سایهبانها بررسی گردیده که ترکیبی از موقعیت سایهبان (داخلی و خارجی)، جهتگیری ساختمان، استراتژیهای کنترلی سایهبان، نقاط تنظیم، و شرایط اقلیمی هستند. متغیرهای خروجی شامل بار سرمایشی، بار گرمایشی، بار الکتریکی روشنایی، مصرف کل انرژی، و ساعات نارضایتی حرارتی و بصری ساکنان میشوند. مدلسازی انرژی با استفاده از نرمافزار انرژی پلاس انجام شده و برای تحلیل و بهینهسازی طراحی، از ابزار جیپلاس استفاده شده است. برای پیشبینی مصرف انرژی ساختمان و ارزیابی تأثیر متغیرهای مختلف، مدل یادگیری ماشین جنگل تصادفی به کار رفته است و برای تفسیر نتایج و تعیین میزان اهمیت هر ویژگی در پیشبینیهای مدل، از الگوریتم SHAP استفاده شده است. نتایج و جمعبندی: بنابر نتایج این تحقیق، استفاده از استراتژیهای کنترلی بهینه برای سایهبانها میتواند بهطور قابلتوجهی مصرف انرژی را کاهش دهد. در تابستان، کاهش مصرف انرژی در شهرهای بندرعباس، یزد، ساری، و تبریز بهترتیب 18٫8، 35٫2، 37٫8، و 45٫5٪ بود و در زمستان نیز کاهش مصرف انرژی بهترتیب 4٫3، 0٫8، 2٫7، و 1٫5٪ ثبت شد. همچنین، طراحی بهینۀ سایهبانها باعث کاهش قابلتوجه نارضایتی حرارتی و بصری ساکنان میگردد؛ بهطور نمونه، در تابستان زمان مطالعه، نارضایتی حرارتی در تبریز تا 64٫2٪ و نارضایتی بصری در همۀ شهرها تا 100٪ کاهش داشت. طبق این یافتهها، انتخاب استراتژی کنترلی مناسب و آستانههای فعالسازی مؤثر میتواند مصرف انرژی را کاهش دهد و آسایش حرارتی و بصری ساکنان را بهبود بخشد. در تابستان، سایهبانهای خارجی با کنترل دمای بیرون در بندرعباس و سایهبانهای خارجی با کنترل دمای داخل در یزد و تبریز بهترین گزینهها از نظر کاهش مصرف انرژی و افزایش آسایش ساکنان شناخته شدند. در زمستان نیز بهترین سناریوها شامل کنترلکنندههای مبتنی بر دمای بیرون با تنظیمکنندگی متناسب برای هر اقلیم بودند، نتایج این تحقیق نشاندهندۀ اهمیت انتخاب استراتژی کنترلی متناسب با شرایط اقلیمی و نیازهای خاص هر منطقه است که میتواند در طراحی پایداری و بهینهسازی مصرف انرژی در ساختمانهای مسکونی استفاده شود. | ||
| کلیدواژهها | ||
| کنترل سایهبان؛ مصرف انرژی؛ آسایش حرارتی و بصری؛ EnergyPlus؛ یادگیری ماشین جنگل تصادفی؛ الگوریتم SHAP | ||
| عنوان مقاله [English] | ||
| Optimising Energy Performance and Thermal Comfort with Shading Control Scenarios in Bandar Abbas, Yazd, Sari, and Tabriz Using Parametric Analysis and Machine-Learning | ||
| نویسندگان [English] | ||
| Fateme Akhlaghinezhad1؛ Hadi Bagheri Sabzevar2 | ||
| 1PhD Candidate,, Faculty of Architecture and Urban Planning, Shahid Beheshti University, Tehranm, Iran | ||
| 2Assistant Professor, Faculty of Architecture and Urban Planning, Hakim Sabzevari University, Sabzevar, Iran | ||
| چکیده [English] | ||
| Background and objectives: The growing demand for energy efficiency in buildings has become a central objective for sustainable development, as the building sector is expected to significantly raise its share of global energy consumption by 2040. Shading systems can therefore play a crucial role by controlling solar heat gain, and thus reducing the cooling and heating loads. This study aims to identify optimal shading control strategies by considering variables such as shading position, building orientation, and climate conditions. EnergyPlus and jEPlus are employed here for parametric modelling and optimisation of various shading scenarios across four Iranian cities: Bandar Abbas, Yazd, Sari, and Tabriz. The primary objective is to reduce energy consumption and enhance both thermal and visual comfort for occupants, with a specific focus on assessing the impact of appropriate shading control strategies in achieving these goals. Materials and Methods: This study utilises a standard residential room model similar to Test Case 600, as outlined in the ANSI/ASHRAE 140-2017 standard. A total of 19 different shading control scenarios were analysed, incorporating varieties in shading position (internal or external), building orientation, shading control strategies, activation thresholds, and climate conditions. The predictive variables considered include cooling load, heating load, electrical lighting load, total energy consumption, and the number of hours of thermal discomfort (PPD index) and visual discomfort (DGI index) experienced by the occupants. Energy modelling was performed using EnergyPlus, while parametric design optimisation is conducted with jEPlus. To predict energy consumption and assess the influence of various parameters, a Random Forest machine learning model was employed. Additionally, the SHAP (SHapley Additive exPlanations) algorithm was used to interpret the model’s predictions, providing a detailed understanding of the contribution of each input variable. Results and conclusion: The results demonstrate that optimised shading control strategies can significantly reduce energy consumption. In the summer, total energy use decreased by 18.8% in Bandar Abbas, 35.2% in Yazd, 37.8% in Sari, and 45.5% in Tabriz. In winter, reductions ranged from 0.8% in Yazd to 4.3% in Bandar Abbas. Additionally, optimised shading design and control led to a substantial reduction in both thermal and visual discomfort. For instance, in Tabriz, thermal discomfort decreased by 64.2%, while visual discomfort was reduced by up to 100% across all cities. The study concludes that selecting the appropriate shading control strategy and activation threshold not only reduces energy consumption but also enhances both thermal and visual comfort for building occupants. Specifically in the summer, external shading with outdoor temperature control in Bandar Abbas, and external shading with indoor temperature control in Yazd and Tabriz, were identified as the most effective strategies for reducing energy consumption and improving comfort. In winter, the most effective strategy involves controls based on outdoor temperature, with varying activation thresholds tailored to each climate. Overall, the study emphasises the critical importance of climate-adapted control strategies in designing energy-efficient and comfortable residential buildings, thereby contributing to the advancement of sustainable architecture across diverse climatic regions. | ||
| کلیدواژهها [English] | ||
| Shading control, Energy consumption, Thermal and visual comfort, EnergyPlus, Random Forest machine learning, SHAP algorithm | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 512 تعداد دریافت فایل اصل مقاله: 398 |
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