Optimisation of orientation of Photovoltaic cells to match electricity demand was carried out for Adelaide. This study involves analysis of solar radiation and temperature time series as well as stochastic programming. Time series were split into 2 parts, deterministic and stochastic components, and analysed separately by Fourier series and Box and Jenkins techniques respectively. It is found that the stochastic component for daily averaged solar radiation and temperature series is best described by the first and second order autoregressive process respectively. In addition, Monte-Carlo simulation was used in order to generate a number of sequences of synthetic solar radiation, temperature and demand series. These are later used as inputs for an Excel spreadsheet model, developed for the project, to find the optimal slope and azimuth for each generation, using stochastic programming. The results, set of slopes and azimuths, are analysed in order to make a general recommendations for the optimal orientation of Photovoltaic cells to match electricity demand for Adelaide. We will describe the software tool that we have developed, and show how it can be utilised to perform a similar analysis for any location, and varying objectives. For instance, one may want to optimise the performance over a year, summer or indeed, only for the hours of peak demand in summer.