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We derive analytic approximations to the solutions of the Lane-Emden equation, a basic equation in Astrophysics that describes the Newtonian equilibrium structure of a self-gravitating polytropic fluid sphere. After recalling some basic results, we focus on the construction of rational approximations, discussing the limitations of previous attempts, and providing new accurate approximate solutions. © 2015 Elsevier B.V.

Accurate and robust short-term load forecasting plays a significant role in electric power operations. This paper proposes a variant of genetic programming, improved by incorporating semantic awareness in algorithm, to address a short term load forecasting problem. The objective is to automatically generate models that could effectively and reliably predict energy consumption. The presented results, obtained considering a particularly interesting case of the South Italy area, show that the proposed approach outperforms state of the art methods. Hence, the proposed approach reveals appropriate for the problem of forecasting electricity consumption. This study, besides providing an important contribution to the energy load forecasting, confirms the suitability of genetic programming improved with semantic methods in addressing complex real-life applications. © 2014 Elsevier B.V.

Air temperature is an effective predictor for electricity demand, especially during hot periods where the need of electric air conditioning can be high. This paper presents for the first time an assessment of the use of seasonal climate forecasts of temperature for medium-term electricity demand prediction. The retrospective seasonal climate forecasts provided by ECWMF (European Centre for Medium-Range Weather Forecasts) are used to forecast the June-July Italian electricity demand for the period 1990-2007.We find a relationship between summer (June-July) average temperature patterns over Europe and Italian electricity demand using both a linear and non-linear regression approach. With the aim to evaluate the potential usefulness of the information contained into the climate ensemble forecast, the analysis is extended considering a probabilistic approach.Results show that, especially in the Center-South of Italy, seasonal forecasts of temperature issued in May lead to a significant correlation coefficient of electricity demand greater than 0.6 for the summer period. The average correlation obtained from seasonal forecasts is 0.53 for the temperature predicted in May and 0.19 for the predictions issued in April for the linear model, while the non-linear approach leads to the coefficients of 0.62 and 0.36 respectively. For the probabilistic approach, seasonal forecasts exhibit a positive and significant skill-score in predicting the demand above/below the upper/lower tercile in many regions.This work is a significant progress in understanding the relationship between temperature and electricity demand. It is shown that much of the predictable electricity demand anomaly over Italy is connected with so-called heat-waves (i.e. long lasting positive temperature anomalies) over Europe. © 2014 Elsevier Ltd.

Electricity demand forecasting is a critical task for energy management of power grids. Due to the wide use of refrigeration and residential air-conditioning devices, electricity demand in Italy is influenced by weather conditions, especially during summer. This paper performs daily load forecasting for Italy through statistical modeling with the aim of studying the influence of temperature. The actual capability of available weather forecasts to contribute in predicting electricity loads is evaluated by using weather data from numerical weather prediction (NWP) models. Time-series models have been used and compared with a naive predictor on working-days daily load during June and July in years 2003-2009 considering lead-times between one and five days. Results are analyzed both at the national level and at regional scale, using unprecedented historical load data provided by the Italian transmission grid manager. It is shown that the use of weather data provided by NWP models leads to performance improvements, especially for the hottest areas where the use of electricity is more heavily influenced by temperature. Furthermore, by observing the gap between load forecast models using reanalysis and operational forecast weather data we can obtain some clues about the limitations of the weather forecast models we used on specific geographic areas in Italy. © 2013 Elsevier B.V.

We identified the recA gene of the moderately thermophilic bacterium Streptococcus thermophilus and investigated the role of its product in the adaptation to heat shock and nutrient starvation. Expression of recA was required for optimal viability and normal cell morphology upon induction of both stresses. Normal induction of GroEL and ClpL in a recA knock-out mutant suggests that the RecA role in heat shock and nutrient starvation response of S. thermophilus is independent from the intracellular accumulation of these stress-specific chaperones.