Our demand forecast assessment will include reviewing the technical elements of a NSP's forecasting approaches. We will assess, for example, weather correction approaches and regression techniques for rigor and supporting evidence. We will also carry out data validation and testing of the trends that underpin the demand forecasts. This work may include checking customer number growth in different regions of the network, or cross checking data from the NSP with other data sources. We will assess whether the NSP considered a variety of sources for its inputs, and whether the input selection process potentially introduces bias to the demand forecast. We will likely review a sample of the NSPs' spatial demand forecasts given a full review of all spatial forecasts is infeasible. In distribution determinations, for example, we will likely review in detail the demand forecasts and forecasting approaches for a sample of zone substations. Preliminary analysis, including discussions with the NSP and other stakeholders, will likely inform the appropriate approach for selecting zone substations for closer review. We may target our sample to include those zone substations associated with a relatively high capex forecast over the next regulatory control period. Alternatively, we may choose a random sample or a mix of targeted and random sampling. The extent to which we extrapolate our findings on the sample to all other zone substations will depend on the issues that arise and the nature of the NSP's forecast. Our detailed review will assess whether issues appear common in the forecast, or are confined to one part of the network. We may find, for example, an unreasonable demand forecast for only one zone substation in the sample. If so, we would not extrapolate adjustments for that zone substation to other zone substations. If we find a more general issue (such as reconciliation between the top down and bottom up forecasts), then we will likely infer findings from the detailed review to the rest of the spatial forecasts. Demand forecast assessment principles We will assess whether a NSP's approach exhibits the following principles of best practice demand forecasting: accuracy and unbiasedness transparency and repeatability incorporation of key drivers weather normalisation model validation and testing
Our demand forecast assessment will include reviewing the technical elements of a NSP's forecasting approaches. We will assess, for example, weather correction approaches and regression techniques for rigor and supporting evidence. We will also carry out data validation and testing of the trends that underpin the demand forecasts. This work may include checking customer number growth in different regions of the network, or cross checking data from the NSP with other data sources. We will assess whether the NSP considered a variety of sources for its inputs, and whether the input selection process potentially introduces bias to the demand forecast. We will likely review a sample of the NSPs' spatial demand forecasts given a full review of all spatial forecasts is infeasible. In distribution determinations, for example, we will likely review in detail the demand forecasts and forecasting approaches for a sample of zone substations. Preliminary analysis, including discussions with the NSP and other stakeholders, will likely inform the appropriate approach for selecting zone substations for closer review. We may target our sample to include those zone substations associated with a relatively high capex forecast over the next regulatory control period. Alternatively, we may choose a random sample or a mix of targeted and random sampling. The extent to which we extrapolate our findings on the sample to all other zone substations will depend on the issues that arise and the nature of the NSP's forecast. Our detailed review will assess whether issues appear common in the forecast, or are confined to one part of the network. We may find, for example, an unreasonable demand forecast for only one zone substation in the sample. If so, we would not extrapolate adjustments for that zone substation to other zone substations. If we find a more general issue (such as reconciliation between the top down and bottom up forecasts), then we will likely infer findings from the detailed review to the rest of the spatial forecasts. Demand forecast assessment principles We will assess whether a NSP's approach exhibits the following principles of best practice demand forecasting: accuracy and unbiasedness transparency and repeatability incorporation of key drivers weather normalisation model validation and testing
AER%20Explanatory%20statement%20-%20draft%20expenditure%20forecast%20assessment%20guideline%20-%20August%202013.DOCX