Volume 9, Issue 3 (September 2024)
J Environ Health Sustain Dev 2024, 9(3): 2354-2368 |
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Sarsangi A, Toomanian A, Neysani Samany N, Kiavarz M, Saraei M H. Investigating the Impact of Environmental Factors on Electricity Consumption Using Spatial Data Mining and Artificial Neural Network: A Case Study in Yazd City. J Environ Health Sustain Dev 2024; 9 (3) :2354-2368
URL: http://jehsd.ssu.ac.ir/article-1-749-en.html
URL: http://jehsd.ssu.ac.ir/article-1-749-en.html
Alireza Sarsangi 
, Ara Toomanian * , Najmeh Neysani Samany , Majid Kiavarz , Mohammad Hossein Saraei
, Ara Toomanian * , Najmeh Neysani Samany , Majid Kiavarz , Mohammad Hossein Saraei
Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran
Abstract: (234 Views)
Introduction: Modeling energy demand in different energy consuming sectors is a crucial measure for effective management of the energy sector and appropriate policies to increase productivity. The rising importance of energy resources in economic development is evident. Sustainable energy use is crucial for environmental protection and social progress. Understanding the factors affecting energy consumption is essential for effective energy management. Therefore, the purpose of the current study is to investigate the impact of environmental factors on household electricity consumption in Yazd city.
Materials and Methods: In the present research, various environmental factors affecting electricity consumption, including air pollution, air temperature in homes, ground surface temperature, and green space were investigated. The effects of these factors on electricity consumption of subscribers were investigated with ANN and apriori methods.
Results: Among the environmental factors, the distance to the regional park, the area of the park, and the amount of vegetation at a distance of 300m have the greatest impact, respectively, and the average summer air temperature, the amount of vegetation at a radius of 500 m, the distance from the local park, and the average summer NDVI have had the smallest effect. Unlike neural network methods, apriori presents relationships between parameters affecting electricity consumption transparently in the form of rules.
Conclusion: It's used to identify the most frequently occurring elements and meaningful associations in a dataset. Greenspace can be a mitigation strateegy for reduction of energy consumption.
Materials and Methods: In the present research, various environmental factors affecting electricity consumption, including air pollution, air temperature in homes, ground surface temperature, and green space were investigated. The effects of these factors on electricity consumption of subscribers were investigated with ANN and apriori methods.
Results: Among the environmental factors, the distance to the regional park, the area of the park, and the amount of vegetation at a distance of 300m have the greatest impact, respectively, and the average summer air temperature, the amount of vegetation at a radius of 500 m, the distance from the local park, and the average summer NDVI have had the smallest effect. Unlike neural network methods, apriori presents relationships between parameters affecting electricity consumption transparently in the form of rules.
Conclusion: It's used to identify the most frequently occurring elements and meaningful associations in a dataset. Greenspace can be a mitigation strateegy for reduction of energy consumption.
Keywords: Artificial neural network, Space syntax, Electricity consumption, Remote sensing, Spatial data mining, Yazd City
Type of Study: Original articles |
Subject:
Environmental Health, Sciences, and Engineering
Received: 2024/05/25 | Accepted: 2024/07/10 | Published: 2024/10/1
Received: 2024/05/25 | Accepted: 2024/07/10 | Published: 2024/10/1
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