Design of a Multi-Objective Decision-Making Algorithm for Balancing Energy Saving and Mental Health in Cold-Climate Housing (Case Study : Urmia City, Iran)

Zarafshan, Ataollah

doi:10.22034/ijumes.2026.2093774.1381

Design of a Multi-Objective Decision-Making Algorithm for Balancing Energy Saving and Mental Health in Cold-Climate Housing (Case Study : Urmia City, Iran)

Document Type : Case Study

Author

Ataollah Zarafshan

Department of Geography and Urban Planning, Payame Noor University, Tehran, Iran

10.22034/ijumes.2026.2093774.1381

Abstract

The accelerating urbanization of Iran’s cold regions, coupled with rising heating energy demand, has created a twofold challenge in the housing sector: mounting pressure on energy resources and the detrimental impact of inadequate indoor environments on residents’ mental health. This study aims to design a multi objective decision making algorithm that reconciles energy conservation with mental health preservation in cold climate housing. A mixed methods research design was adopted, with Urmia City selected as the case study. Empirical data were gathered through parametric energy simulations using HOTR 2022 software and the administration of the DASS 21 mental health inventory, and were subsequently analyzed through an integrated AHP TOPSIS framework reinforced by Monte Carlo stochastic simulation. The findings reveal that the optimal equilibrium is achieved at an indoor temperature of 20–22°C, which reduces heating energy consumption by 28.7% relative to baseline conditions while concurrently improving mental health scores by 34.2%. Cross validation against an independent dataset demonstrated strong predictive accuracy, yielding coefficients of determination of 0.89 and 0.83, alongside root mean square errors of 0.75°C and 8.4 kWh/m²·year for temperature and energy predictions, respectively. The proposed algorithm provides a three dimensional decision matrix that enables intelligent and adaptive regulation of heating systems. By integrating principles of sustainable architecture, environmental psychology, and energy optimization, this research addresses a critical gap in the existing literature - namely, the concurrent incorporation of quantitative energy metrics and qualitative psychological indicators - and offers a pragmatic decision support framework for housing policy formulation in cold climate contexts.

Graphical Abstract

Design of a Multi-Objective Decision-Making Algorithm for Balancing Energy Saving and Mental Health in Cold-Climate Housing (Case Study : Urmia City, Iran)

Highlights

· AHP-TOPSIS-Monte Carlo algorithm balances energy and mental health in cold housing.

· 20–22°C range cuts energy by 28.7% and improves mental health by 34.2%.

· Thermal satisfaction fully mediates indoor temperature-stress relationship.

· Dual-peak heating saves 22.3% energy vs. continuous operation without health loss.

Keywords

Cold-Climate housing

Decision-Making Algorithm

energy saving

mental health

Multi-Objective balancing

Urmia City

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