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Spatio-temporal modelling of solar photovoltaic adoption: An integrated neural networks and agent-based modelling approach

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  • Alderete Peralta, Ali
  • Balta-Ozkan, Nazmiye
  • Longhurst, Philip

Abstract

This paper investigates the spatio-temporal patterns of solar photovoltaic (PV) adoption, solving the ongoing need to inform the management of the distribution networks with spatially explicit estimations of PV adoption rates. This work addresses a key limitation of agent-based models (ABMs) that use rule or equation-based decision-making. It achieves this by adopting an aggregated definition of the agents using artificial neural networks (ANN) as the criteria for decision-making. This novel approachdraws from both ABM and Spatial Regression methods. It incorporates spatial and temporal dependencies as well as social dynamics that drive the adoption of PVs. Consequently, the model yields a more realistic characterisation of decision-making whilst reflecting individual behaviours for each location following the real-world layout. The model utilises the ANN’s approximation capabilities to generate knowledge from historical PV data, as well as adapt to changes in data trends. First, an autoregressive model is developed. This is then extended to capture the population heterogeneity by introducing socioeconomic variables into the agent’s decision-making. Both models are empirically validated and benchmarked against the Bass Model.

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  • Alderete Peralta, Ali & Balta-Ozkan, Nazmiye & Longhurst, Philip, 2022. "Spatio-temporal modelling of solar photovoltaic adoption: An integrated neural networks and agent-based modelling approach," Applied Energy, Elsevier, vol. 305(C).
    • Handle: RePEc:eee:appene:v:305:y:2022:i:c:s0306261921012599
    DOI: 10.1016/j.apenergy.2021.117949
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