residuals. NMBE Normalised mean-bias error (see Eq. 5). A measure of whether the model tends to under or over-predict on average. >-0.5 and <0.5% N Residual autocorrelation Based on the Durbin-Watson test statistic for residual autocorrelation. >0.5 and <3.5 Y Seasonal trend Determines whether there is a statistically meaningful trend for the model residuals based on season. Based on the method outlined in [17]. All segments P>0.01 or R2 < 0.65 Y Time trend Determines whether there is a statistically meaningful trend for the model residuals over time based on the method outlined in [17]. All segments P>0.01 or R2 < 0.65 Y Over & underestimated months No. of whole months where the difference between the mean daily model and actual consumption is greater than ±3 standard deviations from the mean. ≤2 underestimated Y Missing data Whole days are classed as missing when missing data exceeds a daily threshold. The missing data criteria is then calculated based on the percentage of days in the months classed as missing. Excluded days are not counted towards missing data. Daily missing threshold 20%. Monthly threshold <30%. Y Excluded data As per the missing data check. However, excluded days are counted towards missing data. Daily threshold 20%. Monthly threshold <25%. Y Outliers Difference between model and actual daily energy consumption is greater than ±3 standard deviations from the mean <5 outlier days N PV Fits a regression model for daily normalised energy use as a function of ambient temperature, temperature squared and irradiance. Compares the coefficient of the irradiance term for magnitude and statistical significance. Coefficient < -2.5E-4 & p < 1E-22 or Coefficient <-4E-4 N ≤2 overestimated The R2, CVRMSE and NMBE metrics are evaluated over the fitted data at the frequency corresponding to the fitting model. For example, for the interval model, hourly values are used. For the daily model, daily values are used and so on. R2 gives a measure of the amount of variability in the data explained by the model with higher values corresponding to better performance. R2 is calculated by; 42 | CSIRO Australia’s National Science Agency