Abstract
It is noted that human behaviour changes can have a significant impact on energy consumption, however, qualitative study on such an impact is still very limited, and it is necessary to develop the corresponding mathematical models to describe how much energy savings can be achieved through human engagement. In this paper a mathematical model of human behavioural dynamic interactions on a social network is derived to calculate energy savings. This model consists of a weighted directed network with time evolving information on each node. Energy savings from the whole network is expressed as mathematical expectation from probability theory. This expected energy savings model includes both direct and indirect energy savings of individuals in the network. The savings model is obtained by network weights and modified by the decay of information. Expected energy savings are calculated for cases where individuals in the social network are treated as a single information source or multiple sources. This model is tested on a social network consisting of 40 people. The results show that the strength of relations between individuals is more important to information diffusion than the number of connections individuals have. The expected energy savings of optimally chosen node can be 25.32% more than randomly chosen nodes at the end of the second month for the case of single information source in the network, and 16.96% more than random nodes for the case of multiple information sources. This illustrates that the model presented in this paper can be used to determine which individuals will have the most influence on the social network, which in turn provides a useful guide to identify targeted customers in energy efficiency technology rollout programmes.
Highlights
In the UK, the residential sector accounts for 31% of total energy consumption except non-energy use in 2013 [1]
The remainder of the paper is structured as follows, a mathematical model quantifying energy savings achieved through network interaction and the case study is provided in Section 2, the calculation results from a survey is discussed in Section 3, and some conclusions are made in the last section
The designed model in this paper quantifies the impact between individuals and uses the expected energy savings as a comparable indicator to evaluate the impact of human interactions on energy consumption
Summary
In the UK, the residential sector accounts for 31% of total energy consumption except non-energy use in 2013 [1]. In the mass rollout of energy efficiency products, such as the rollout of solar water heaters in many countries, there are some selected nodes within the social network with earlier installation of efficient products earlier than others for various reasons, say, a positive response to new technologies or free trial provided by suppliers These nodes will benefit from the new energy efficient products and will spread such information among their friends and relatives. The remainder of the paper is structured as follows, a mathematical model quantifying energy savings achieved through network interaction and the case study is provided, the calculation results from a survey is discussed, and some conclusions are made in the last section The remainder of the paper is structured as follows, a mathematical model quantifying energy savings achieved through network interaction and the case study is provided in Section 2, the calculation results from a survey is discussed in Section 3, and some conclusions are made in the last section
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