Abstract
This paper presents a methodology to cascade probabilistic models and agent-based models for fine-grained data simulation, which improves the accuracy of the results and flexibility to study the effect of detailed parameters. The methodology is applied on residential energy consumption behaviour, where an agent-based model takes advantage of probability distributions used in probabilistic models to generate energy consumption of a house with a focus on energy waste. The implemented model is based on large samples of real data and provides flexibility to study the effect of social parameters on the energy consumption of families. The results of the model highlighted the advantage of the cascading methodology and resulted in two domain-specific conclusions: (1) as the number of occupants increases, the family becomes more efficient, and (2) young, unemployed, and part-time occupants cause less energy waste in small families than full-time and older occupants. General insights on how to target families with energy interventions are included at last.
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Abdallah, F., Basurra, S., Gaber, M.M. (2018). Cascading Probability Distributions in Agent-Based Models: An Application to Behavioural Energy Wastage. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2018. Lecture Notes in Computer Science(), vol 10842. Springer, Cham. https://doi.org/10.1007/978-3-319-91262-2_44
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DOI: https://doi.org/10.1007/978-3-319-91262-2_44
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