Modelling and forecasting PV production in the absence of behind-the-meter measurements

Abstract

This paper deals with hourly day-ahead prediction of the net electricity load at nine photovoltaic installations in a Swedish regional electricity network. The objective of the study was to develop, test, and evaluate a set of methods to predict the contribution of PV power to the grid without knowing the production and consumption “behind-the-meter.” An indirect and a direct approach for prediction of the net load were evaluated. For the indirect approach, a model of the gross production was first estimated based on the open-source software PVLIB. The model was then used to predict the net load given a forecast of the gross consumption. Since we lacked a model of the latter, we used a “perfect forecast,” in terms of measured gross consumption, to estimate the performance of this approach. In the direct approach, a model of the net load was estimated using either linear regression or an artificial neural network. Here, the model was used for prediction of the net load without any information about the gross consumption. Both approaches rely on information from a numerical weather prediction model together with net load measurements from the previous day. Forecasts using the indirect approach with perfect information about the gross consumption resulted in a normalized (with installed nominal power) RMSEn of 11%. The direct approach with the artificial neural network also resulted in an RMSEn of 11%, even though it did not have any information from behind the meter. Linear regression had an RMSEn of 12%.