واکاوی عوامل موثر در مصرف انرژی الکتریسیته با استفاده از قوانین انجمنی (مطالعه موردی: شهر یزد) | ||
| کاوش های جغرافیایی مناطق بیابانی | ||
| دوره 12، شماره 2، اسفند 1403، صفحه 19-40 اصل مقاله (1.7 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22034/grd.2025.22527.1640 | ||
| نویسندگان | ||
| علیرضا سرسنگی علی آباد1؛ آرا تومانیان* 2؛ مجید کیاورز مقدم3؛ نجمه نیسانی سامانی3؛ محمد حسین سرائی4 | ||
| 1دانشجوی دکتری رشته سنجش از دور و سامانه اطلاعات جغرافیایی، دانشکده جغرافیا، دانشگاه تهران. | ||
| 2دانشیار گروه سنجش از دور وGIS، دانشکده جغرافیا، دانشگاه تهران | ||
| 3دانشیار گروه سنجش از دور وGIS، دانشکده جغرافیا، دانشگاه تهران | ||
| 4استاد، گروه جغرافیا، دانشگاه یزد. | ||
| چکیده | ||
| درک عمیق از عوامل موثر بر مصرف برق، پیشنیازی ضروری برای تدوین سیاستهای انرژی کارآمد، بهبود مدیریت مصرف و کاهش اثرات زیستمحیطی ناشی از تولید برق است. هدف از این تحقیق بررسی عوامل مؤثر بر مصرف برق منازل مسکونی با استفاده از الگوریتم قوانین انجمنی Apriori است. در این مطالعه، دادههای مربوط به مصرف برق منازل در دورههای زمانی دو ماهه مربوط به بازه زمانی 1395 تا 1398 جمعآوری شد. مولفه های کالبدی شامل مساحت پارسل، مساحت ساختمان، مساحت حیاط، ارتفاع ساختمان، نوع بافت شهری(جدید، تاریخی و فرسوده) و مولفههای پیکرهبندی فضایی نظیر عمق محلی، مولفه کنترل، هم پیوندی و اتصال برای هریک از پارسلهای شهری (35361 پارسل) برآورد شد. سپس با استفاده از الگوریتم Apriori تحلیل شدند تا روابط پنهان و الگوهای تأثیرگذار بر مصرف برق شناسایی شوند. نتایج نشان داد که عواملی مانند مساحت ساختمان، نوع بافت شهری، ارتفاع ساختمان، کنترل و همپیوندی نقش مهمی در تعیین میزان مصرف برق دارند. علاوه بر این، با استخراج قوانین انجمنی، برخی از ترکیبهای ویژگیها که موجب افزایش یا کاهش مصرف برق میشوند، شناسایی شدند. استفاده از این روش میتواند به مدیریت بهینه مصرف انرژی در منازل مسکونی کمک کند و به افراد و نهادهای مسئول در کاهش هزینهها و بهبود رفتارهای مصرفی یاری رساند. در نهایت، این تحقیق بر مزایای کاربرد الگوریتمهای دادهکاوی در تحلیل و پیشبینی مصرف انرژی تأکید میکند و محدودیتها و چالشهای مرتبط با استفاده از این روش را نیز بررسی مینماید. | ||
| کلیدواژهها | ||
| اپریوری؛ داده کاوی مکانی؛ بافت شهری؛ کاهش مصرف انرژی؛ طراحی شهری پایدار | ||
| عنوان مقاله [English] | ||
| Investigating factors affecting electricity energy consumption using association rules(Case study: Yazd city) | ||
| نویسندگان [English] | ||
| Alireza Sarsangi Aliabad1؛ Ara Toomanian2؛ Majid Kiavarz3؛ Najme Neysani Samani3؛ Mohammad Hossein Saraei4 | ||
| 1Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran | ||
| 2Associate Professor, Remote Sensing and GIS Department, Faculty of Geography, University of Tehran | ||
| 3Associate Professor, Remote Sensing and GIS Department, Faculty of Geography, University of Tehran | ||
| 4Professor, Department of Geography, Yazd University. | ||
| چکیده [English] | ||
| Extended Abstract: 1. Introduction Electricity is an essential input for all production systems and a necessity for all modern families. Hence, relevant energy policies are needed to induce efficient electricity consumption in the residential sector in many countries due to the effects of global warming and security of energy supply. Forecasting electricity demand at a regional or national level is crucial for planning to ensure optimal energy management. Various factors influence household consumption patterns. Factors such as employment rate, residential area, distance from green space, etc. affect electricity consumption. The purpose of this study is to investigate the impact of various factors on electricity consumption in residential homes in Yazd city. The results of this study will be useful for making management decisions for planning to reduce electricity consumption. 2. Research Methodology The present study was conducted in the city of Yazd, which has a hot and dry climate and is extremely hot in the summer. Data on electricity consumption of Yazd city subscribers was obtained from the provincial electricity distribution company for the years 2016 to 2019. Data related to the city's buildings, such as (current use, building height, area, building shape, and building age), as well as streets, existing street widths, and the location of parks and green spaces, were obtained from the municipality. Spatial configuration indices including: connectivity, depth, coherence and control were estimated. The urban physical parameters of the components of parcel area, building area, yard area, building height, building volume were calculated. Then, association rules were used to examine the existing relationships. Spatial Association Rules are a set of rules that describe the relationships between different features in spatial data. These rules are a capability to find unknown relationships in spatial data. Spatial association rules are rules that indicate the implication of a set of features on another set of features in a spatial database. These rules are introduced to discover the rules between products in large-scale transactional data. 3. Results and discussion Residential electricity consumption data was analyzed using Moran's spatial autocorrelation index and based on Euclidean distance. The results of the study of hot and cold spots of residential electricity consumption data in the study area showed that the distribution of electricity consumption in residential homes is asymmetrical. That is, the number of homes with very high electricity consumption is greater than the number of homes with very low electricity consumption.In total, 3.2 percent of the number of parcels in the region is made up of Low_High outliers and 4.7 percent is High_Low. In the present study, the Apriori algorithm was used. The Apriori algorithm is known as one of the main methods in data mining for discovering association rules. The results of the rule review using Apriori showed that in rule one: buildings with a height of 5 to 8 meters that are located in a new urban context are most likely (93%) to have an annual electricity consumption of more than 3,500 units. Rule two: buildings that are located in a new urban context and their control is less than 1 are most likely (87%) to have an annual electricity consumption of more than 3,500 units. Rule three: buildings that are located in parcels with an area of 150 to 250 square meters and a local connectivity of 2-3 are most likely (74%) to have an annual electricity consumption of more than 3,500 units. Rule four: buildings that are located in parcels with an area of 150 to 250 square meters and in a new urban context and with a yard area of less than 75 square meters are most likely (61%) to have an annual electricity consumption of more than 3,500 units. 4. Conclusion Association rules are able to extract patterns that cannot be easily identified by traditional methods and provide useful information for optimizing energy consumption.One of the major challenges in using association rules in big data is the need for time-consuming and resource-intensive processing, especially when the data is complex and contains a large number of features. Association rules are usually designed for discrete data, and for numerical data, complex preprocessing such as converting the data to categorical values may be required. Also, the appropriate selection of parameters such as minimum support and confidence can be difficult and have a significant impact on the quality and applicability of the extracted results. It is suggested that in future studies, hourly electricity consumption data should be used if possible so that the effects of more factors can be examined. - | ||
| کلیدواژهها [English] | ||
| Apriori, spatial data mining, urban texture, energy consumption reduction, sustainable urban design | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 326 تعداد دریافت فایل اصل مقاله: 174 |
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