Modelling Consumer Roles in the Electricity System

The consumer role has been difficult to map to Energy System planning due to the difficulty of measuring it. Research methodology was a discovery and enquiry into the question of behaviour in response to energy policy. There was a wealth of relevant data available and the experiment assessed what correlations there are between that data and the adoption of solar. As this experiment was on an entire population there was no way to assess it on in a controlled experiment, but the quantity of data meant there is a high confidence in the result. The method was: sourcing data; exploratory modelling of PV adoption by postcode; method of analysis; regression to identify influential variables; spatial regression; what we learnt from analysing the solar data by postcode; temporal modelling to forecast consumer actions; autoregressive integrated moving average (ARIMA) modelling results; and then testing the ARIMA model. The ARIMA model is similar to the autoregressive moving average (ARMA) model and is powerful for time-series modelling. This chapter shows how this modelling successfully forecast PV uptake in Australia.

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