Numerical Weather Prediction (NWP) and hybrid ARMA/ANN model to predict global radiation

We propose in this paper an original technique to predict global radiation using a hybrid ARMA/ANN model and data issued from a numerical weather prediction model (ALADIN). We particularly look at the Multi-Layer Perceptron. After optimizing our architecture with ALADIN and endogenous data previously made stationary and using an innovative pre-input layer selection method, we combined it to an ARMA model from a rule based on the analysis of hourly data series. This model has been used to forecast the hourly global radiation for five places in Mediterranean area. Our technique outperforms classical models for all the places. The nRMSE for our hybrid model ANN/ARMA is 14.9% compared to 26.2% for the na\"ive persistence predictor. Note that in the stand alone ANN case the nRMSE is 18.4%. Finally, in order to discuss the reliability of the forecaster outputs, a complementary study concerning the confidence interval of each prediction is proposedComment: Energy (2012)

The Sounds of Science: Listening to Laboratory Practice

Works in science and technology studies (STS) have repeatedly pointed to the importance of the visual in scientific practice. STS has also explicated how embodied practice generates scientific knowledge. I aim to supplement this literature by pointing out how sound and hearing are integral aspects of experimentation. Sound helps define how and when lab work is done, and in what kinds of spaces. It structures experimental experience. It affords interactions between researchers and instruments that are richer than could be obtained with vision alone. And it is a site for tacit knowledge, providing a resource for the replication of results, and the transmission of knowledge, and the construction of social boundaries within instrumental communities