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

Optical properties of cubic and rhombohedral GeTe

Calculations of the optical properties of GeTe in the cubic NaCl and rhombohedral ferroelectric structures are reported. The rhombohedral ferroelectric distortion increases the band gap from 0.11 eV to 0.38 eV. Remarkably, substantial changes in optical properties are found even at high energies up to 5 eV. The results are discussed in relation to the bonding of GeTe and to phase change materials based on it

Prediction of Room Temperature High Thermoelectric Performance in n-type La(Ru,Rh)4Sb12

First principles calculations are used to investigate the band structure and the transport related properties of unfilled and filled 4d skutterudite antimonides. The calculations show that, while RhSb3 and p-type La(Rh,Ru)4Sb12 are unfavorable for thermoelectric application, n-type La(Rh,Ru)4Sb12 is very likely a high figure of merit thermoelectric material in the important temperature range 150-300 K.Comment: 3 pages, 3 figures. To appear, Appl. Phys. Let

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