Power forecasting has received a great deal of attention due to its importance for planning the operations of photovoltaic (PV) system. Compared to other forecasting techniques, the ARIMA time series model does not require the meteorological forecast of solar irradiance that is often complicated. Due to its simplicity, the ARIMA model has been widely discussed as a statistical model for forecasting power output from a PV system. However, the ARIMA model is a data-driven model that cannot take the climatic information into account. Intuitively, such information is valuable for improving the forecast accuracy. Motivated by this, this paper suggests a generalized model, the ARMAX model, to allow for exogenous inputs for forecasting power output. The suggested model takes temperature, precipitation amount, insolation duration, and humidity that can be easily accessed from the local observatory as exogenous inputs. As the ARMAX model does not rely forecast on solar irradiance, it maintains simplicity as the conventional ARIMA model. On the other hand, it is more general and flexible for practical use than the ARIMA model. It is shown that the ARMAX model greatly improves the forecast accuracy of power output over the ARIMA model. The results were validated based on a grid-connected 2.1kW PV system. © 2013 Elsevier Ltd.