Bruno Lacarrière

Pseudo dynamic transitional modeling of building heating energy demand using artificial neural network

This paper presents the building heating demand prediction model with occupancy profile and operational heating power level characteristics in short time horizon (a couple of days) using artificial neural network. In addition, novel pseudo dynamic transitional model is introduced, which consider time dependent attributes of operational power level characteristics and its effect in the overall model performance is outlined. Pseudo dynamic model is applied to a case study of French Institution building and compared its results with static and other pseudo dynamic neural network models. The results show the coefficients of correlation in static and pseudo dynamic neural network model of 0.82 and 0.89 (with energy consumption error of 0.02%) during the learning phase, and 0.61 and 0.85 during the prediction phase, respectively. Further, orthogonal array design is applied to the pseudo dynamic model to check the schedule of occupancy profile and operational heating power level characteristics. The results show the new schedule and provide the robust design for pseudo dynamic model. Due to prediction in short time horizon, it finds application for Energy Services Company (ESCOs) to manage the heating load for dynamic control of heat production system.

Wind Resource Assessment in Europe Using Emergy

In context of increasing use of renewable sources, it is of importance to correctly evaluate the actual sustainability of their implementation. Emergy analysis is one of the possible methods useful for such an assessment. This work aims to demonstrate how the emergy approach can be used to assess the sustainability of wind energy resource in Europe. The Emergy Index of Sustainability (EIS) and the Emergy Yield Ratio (EYR) are used to analyze 90 stations of European regions for three types of wind turbines. To do so, the simplified Chou wind turbine model is used for different set of parameters as: nominal power and size of the wind turbines, and cut-in and cut-out wind speeds. Based on the calculation of the emergy indices, a mapping is proposed to identify the most appropriate locations for an implementation of wind turbines in European regions. The influence of the wind turbine type on the sustainability is also analyzed, in link with the local wind resource. Thus, it is concluded that the emergy sustainability indices are sensitive to the wind turbine design parameters (size, cut-in and cut-out wind speed).