In this study, the outputs from a simple PV performance model were compared to measurements of AC power for three PV systems located across Sydney, Australia. The study aimed to investigate the level of uncertainty and bias of the model when onsite measurements of plane of array (POA) irradiance and module temperature were not available.

The increasing penetration of solar power into the electricity grid has created the need for accurate solar power forecasts for facilitating safe and reliable electricity grid management. Numerical Weather Prediction (NWP) is a common method for forecasting solar irradiance beyond several hours ahead, which is crucial, particularly for applications in...

This presentation investigates the value of using forecast variables from multiple vertical layers of NWP as machine learning inputs in improving the accuracy of solar irradiance forecasts. Moreover, the effects of postprocessing on the NWP models with different spatio-temporal resolution – Global Forecast System (GFS), regional (ACCESS-R) and city (ACCESS-C)...

Solar is an excellent investment for almost all Australian households, but there are a wide range of savings, depending on the characteristics of household electricity consumption, retail tariffs and roof characteristics. Nevertheless, the majority of households installing a wide range of PV system sizes under different types of tariffs can expect a payback of between 3-7 years.

The standard mathematical approach used to calculate photovoltaic (PV) array spacing contains a number of assumptions that limits its use to PV arrays installed on horizontal surfaces. This paper utilises vector analysis to develop a new method to calculate array spacing and potential system size for any combination of PV array and surface tilt and orientation.

Deployment of rooftop photovoltaics (PV) is technically constrained by the availability of suitable roof space as well as by the ability of the distribution network to absorb exported generation.

Rapid growth of distributed small scale Photovolatic (PV) systems has increased the need for tools that can undertake reliable real time monitoring of system performance with the capability to detect and diagnose underperformance at the earliest possible stage. This project refined the system performance algorithms within Solar Analytics, a PV systems monitoring tool offered by Suntech, to improve their accuracy and to test their ability to predict the performance of residential and commercial PV systems.