Thermoelectric properties of β{\beta}-FeSi2_{\text2}

We investigate the thermoelectric properties of ${\beta}$-FeSi$_{\text2}$ using first principles electronic structure and Boltzmann transport calculations. We report a high thermopower for both \textit{p}- and \textit{n}-type ${\beta}$-FeSi$_{\text2}$ over a wide range of carrier concentration and in addition find the performance for \textit{n}-type to be higher than for the \textit{p}-type. Our results indicate that, depending upon temperature, a doping level of 3$\times10{^{20}}$ - 2$\times10{^{21}}$ cm${^{-3}}$ may optimize the thermoelectric performance

A Guide to Solar Power Forecasting using ARMA Models

We describe a simple and succinct methodology to develop hourly auto-regressive moving average (ARMA) models to forecast power output from a photovoltaic solar generator. We illustrate how to build an ARMA model, to use statistical tests to validate it, and construct hourly samples. The resulting model inherits nice properties for embedding it into more sophisticated operation and planning models, while at the same time showing relatively good accuracy. Additionally, it represents a good forecasting tool for sample generation for stochastic energy optimization models