مدل سازی مبتنی بر عامل تصویب روشنایی با کارایی بالا در بخش مسکونی

Due to the wide use of incandescent lighting, residential sector has much lower energy efficiency comparing to commercial sector. However, adoption of compact fluorescent (CFL) and light-emitting diode (LED) technology in residential sector has been slow because of several obstacles such as high price tag, poor public information, and additional cost to achieve favorable lighting features. A deep understanding on consumer’s behavior is needed to support policy development in order to speed up the penetration of CFL and LED in the residential sector. Agent-based modeling (ABM) has been used to capture the dynamics of complex socio-technical systems, and represent a suitable tool. Previous work on ABM of consumer adoption of CFL and LED rely heavily on multi-criteria decision making of the agents. Since light bulbs are not a significant purchase for most households, it is highly possible that customers will not go through complex decision making mechanics. This research establishes an ABM of residential lighting purchase and usage within a hypothetical community and tries to illustrate possible adoption paths under different scenarios. Agents are divided into three groups with different simple decision heuristics when making purchase. Energy consumption and greenhouse gas (GHG) emission from each scenario are calculated and compared. Results of the simulation show that incandescent lamps will eventually fade out of the market even with no policy implemented. After 25 years, annual energy consumption can be reduced by roughly 30% compared to Year 2010. Under best case where incandescent bulbs are banned, the energy consumption reduction can be up to 70%. Among scenarios, incandescent ban and energy saving campaign yield best energy consumption and GHG emission reduction results. LED technology advancement can improve market penetration of LED lighting but has little effect on incandescent fade out. It is also shown that lighting technology retrofitting can achieve higher reduction on electricity consumption and GHG emission than electricity grid improvement.