نوع مقاله : مقاله پژوهشی

نویسندگان

• صلاح ویسی ¹
• سمیه حسینی پور ²
• نگار خیابان‌چیان ³
• سید محمد مهدی حسینی کیا ⁴

¹ دانشیار گروه مهندسی معماری، دانشکده هنر و معماری، دانشگاه کردستان، سنندج، ایران.

² کارشناسی ارشد معماری، دانشکده هنر و معماری، دانشگاه کردستان، سنندج، ایران.

³ دانشجوی دکتری شهرسازی، گروه مهندسی شهرسازی، دانشکده هنر و معماری، دانشگاه کردستان، سنندج، ایران.

⁴ دکتری معماری، دانشکده هنر و معماری، دانشگاه کردستان، سنندج، ایران.

چکیده

کشور ایران با چالش‌های اساسی در زمینه تولید وتأمین انرژی در مقیاس کلان مواجه است. ناترازی انرژی به چالش اصلی کشور تبدیل شده و بر کشاورزی، بازار، صنعت و حتی امنیت ملی هم تأثیر منفی گذاشته است. برآورد نیاز انرژی در مقیاس شهری بر اساس داد‌ه های واقعی یا شبیه‌سازی برمبنای بِنچ مارک‌(نشان)های انرژی، یک ابزار دقیق برنامه‌ریزی و مدیریت مصرف انرژی است. در این پژوهش مصرف انرژی شهر مریوان در سطح ساختمان برآورد شده و سپس بر اساس آنها نقشه‌های دقیق نیاز انرژی شهر تولید شده‌اند. محاسبات پس از انجام غربالگری و پایش داده‌های واقعی با استفاده از دو متغیر مستقلِ مساحت فضاهای کنترل شده و کاربری ساختمان و همچنین متغیر وابسته یعنی مصرف سالانه برق و گاز و مصرف سالانه کل انرژی (مجموع برق و گاز) با مدل­سازی GIS نهایی شدند. نتایج نشان می‌دهد که هر ساختمان با کاربری مشخص، چه میزان انرژی برق وگاز نیاز دارد. نتایج نشان می‌دهد بیشترین میزان مصرف مربوط به بخش میانی شهر بوده و بخش شمالی کمترین انرژی را مصرف کرده است. در مجموع ساختمان‌های مسکونی ده برابر بیشتر از ساختمان‌های غیرمسکونی انرژی مصرف کرده‌اند. تحلیل نقشه‌ها نشان می‎دهد شهر مریوان سالیانه به 2032 مگاوات ساعت انرژی گاز و 117مگاوات ساعت انرژی برق نیاز دارد. بر اساس تحلیل‌های به دست آمده، بیشترین نیاز انرژی برق برابر 41 مگاوات‌ ساعت مربوط به فصل تابستان و کمترین آن برابر 33 مگاوات ساعت مربوط به فصل بهار است. درحالی که بیشترین مصرف گاز 855 مگاوات ساعت در فصل زمستان بوده و کمترین مصرف 239 مگاوات ساعت در فصل تابستان بوده است.

کلیدواژه‌ها

• نقشه انرژی شهر‌
• محاسبه و مدل سازی نیاز انرژی
• معماری و شهر پایدار
• مدیریت شهر هوشمند
• ساختمان کم مصرف انرژی
• بِنچ مارک مصرف انرژی

موضوعات

• مدیریت شهری

عنوان مقاله [English]

Assessment and smart management of energy demands in buildings in Marivan City using energy maps

نویسندگان [English]

• Salah Vaisi ¹
• Somayeh Hosseinipour ²
• Negar Khiabanchian ³
• Seyyed Mohammadmahdi Hosseinikia ⁴

¹ Department of Architecture, Faculty of Art and Architecture, University of Kurdistan, Sanandaj, Iran

² Department of Architecture, Faculty of Art and Architecture, University of Kurdistan, Sanandaj, Iran

³ Department of Urban Planning and Design, Faculty of Art and Architecture, University of Kurdistan, Sanandaj, Iran

⁴ Department of Architecture, Faculty of Art and Architecture, University of Kurdistan, Sanandaj, Iran

چکیده [English]

Highlights

By integrating GIS techniques with real electricity and natural gas consumption data, this study develops detailed and spatially precise urban energy maps that quantify annual demand at the building scale.
The resulting energy maps function as a strategic planning tool, enabling city managers to optimize infrastructure investment, design smart energy policies, and facilitate the transition toward sustainable urban energy systems.

 
Introduction
As Iran continues to confront critical challenges in large-scale energy production, distribution, and long-term resource security, the issue of energy imbalance has become a central concern for national development. Urban areas, which account for a substantial share of global and national energy consumption, require accurate and place-based planning tools that can support evidence-driven policymaking. In this context, the city of Marivan in Kurdistan Province offers a compelling case for investigating the dynamics of urban energy demand due to its unique climatic conditions, heterogeneous building patterns, and expanding urban footprint.
This research seeks to estimate building-level energy consumption and to produce high-resolution energy demand maps for Marivan using an integrated modeling approach. The study also aims to examine how local climatic factors, building function (residential versus non-residential), and physical characteristics influence macro-scale energy consumption patterns. Furthermore, it investigates the ways in which urban energy maps—when grounded in empirical utility data—can serve as a scientific decision-support tool for smart energy management. In doing so, the research contributes to the broader discourse on sustainable energy planning and provides a methodological foundation for developing smart cities in Iran and other regions undergoing similar transitions.
Theoretical Framework
The study is informed by the expanding field of Urban Building Energy Modeling (UBEM), a multidisciplinary framework that integrates building physics, GIS, data science, and urban planning. UBEM enables researchers to estimate and visualize energy consumption at the urban scale by combining both physical characteristics of buildings and empirical utility data.
The conceptual framework of this research employs a hybrid top-down/bottom-up approach. From a top-down perspective, aggregated real-world energy consumption data—captured through electricity and gas billing records—are used to establish statistical relationships between different types of buildings and their energy use profiles. This ensures that the model is rooted in actual consumption behavior rather than relying solely on theoretical assumptions.
The bottom-up component incorporates detailed building attributes, such as conditioned area, form, and functional classification, providing a granular understanding of how spatial and physical conditions influence energy demand. Within this dual approach, the study applies energy benchmarking techniques, drawing from internationally recognized standards such as CIBSE TM46 while also integrating locally calibrated benchmarks tailored to the climatic conditions of the Kurdish highland region.
This integrated theoretical framework ultimately positions urban energy maps as more than visualization tools; rather, they become essential instruments for strategic energy governance. They support policy evaluation, identification of high-consumption hotspots, assessment of energy inefficiencies, and planning for renewable energy deployment—functions that align closely with the principles of smart city development.
Methodology
This applied study employs a quantitative–analytical approach, combining correlation analysis, multiple linear regression (MLR), and spatial modeling.
The statistical population of the study consists of approximately 35,000 buildings, representing 98% of all structures in Marivan. Building characteristics, including conditioned area and land-use classification, were obtained from the Kurdistan Road and Urban Development Department. Real consumption data for electricity and natural gas were acquired directly from utility providers, ensuring a high degree of reliability and spatial accuracy.
Following data screening and organization in Excel and SPSS, a regression model was constructed using two independent variables—conditioned area and building function—to predict the dependent variable, annual energy consumption. The model parameters were subsequently applied within a GIS environment. Using customized Visual Basic (VB) scripts, the calculated energy intensity benchmarks (kWh/m²/year) were assigned to each building footprint, generating spatially explicit predictive maps for annual electricity demand, gas demand, and total combined energy consumption.
Model validation was performed through a comparative analysis between predicted and actual consumption datasets. This validation strengthened the credibility of the model and allowed for minor calibration adjustments to better align with observed consumption patterns across different parts of the city.
Results and Discussion
The findings reveal key insights into Marivan’s urban energy dynamics. The city requires an estimated 2.032 MWh of thermal energy (natural gas) and 117 MWh of electrical energy annually, with significant spatial variability across districts. The central urban area demonstrates the highest energy intensity, reflecting its concentration of commercial activities, dense residential blocks, and aging building stock. In contrast, northern neighborhoods exhibit the lowest consumption levels, partly due to newer construction and lower population density.
Residential buildings emerge as the dominant energy consumers, using nearly ten times more total energy than non-residential buildings due to their numerical prevalence and larger cumulative conditioned area. Seasonal analysis illustrates a clear divergence: peak electricity demand (41 MWh) occurs during the summer months—primarily due to cooling loads—while peak gas consumption (855 MWh) is concentrated in winter, driven by extensive heating needs.
Regression analysis confirms a significant positive correlation between building area and gas consumption, validating the role of building size as a major determinant of annual energy demand. The results underscore the necessity of adopting a differentiated policy approach that targets high-consumption zones and building categories.
Conclusion
This study demonstrates that GIS-based UBEM modeling, when combined with real consumption data and locally calibrated benchmarks, can accurately forecast and map urban energy demand at the building scale. The resulting energy maps provide a powerful decision-support framework for city managers, planners, and energy service companies.
Given Iran’s ongoing energy imbalance, the research emphasizes the need to diversify the urban energy portfolio by gradually transitioning from fossil-fuel dependency toward distributed renewable systems, especially solar energy. The spatial outputs of this research offer a scientific basis for identifying optimal sites for photovoltaic installations and for prioritizing zones requiring energy retrofits.
The study’s methodology is replicable and can be applied to other Iranian cities, promoting the development of smart energy dashboards to monitor, manage, and enhance energy efficiency at the national level. Future research is encouraged to incorporate socioeconomic variables, household behavioral patterns, and climate change scenarios to enrich the predictive capacity of urban energy models.

کلیدواژه‌ها [English]

• City energy map
• Energy demand calculation and modeling
• Sustainable architecture and city
• Smart city management
• Low-energy buildings
• Energy consumption benchmarks