Impact analysis of individual and network factors to household energy savings

Reducing energy consumption in residential use sector can largely improve energy savings especially in this post pandemic era when working from home becomes a favoured option for many people. Each home's energy consumption pattern depends on individual user factors and is also influenced by the networks. In this work, four factors that may affect user's adoption decision of energy Efficient products are investigated including the personal acceptance level, the influence from the connected neighbours, the overall network adoption rate, and the advertisement influence. The personal acceptance level is further modelled taking account of individual factors on household income, family status, age group and employment status. To enable quantitative analysis, a dynamic network model is established in which each household is taken as a node, and a utility measure is defined for decision making that integrates multiple impact factors described by subsystem models. The relative contribution of each factor towards user's decision making is evaluated by its associated weighting. Two population networks are studied, starting from a small network with 40 homes, followed by a large one with one million nodes. Simulation results from both population networks reveal that, among the four factors considered, the overall network adoption rate is most influential to user's decision on adopting energy Efficient products.