Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption

There are several ways to attempt to model a building and its heat gains from external sources as well as internal ones in order to evaluate a proper operation, audit retrofit actions, and forecast energy consumption. Different techniques, varying from simple regression to models that are based on physical principles, can be used for simulation. A frequent hypothesis for all these models is that the input variables should be based on realistic data when they are available, otherwise the evaluation of energy consumption might be highly under or over estimated. In this paper, a comparison is made between a simple model based on artificial neural network (ANN) and a model that is based on physical principles (EnergyPlus) as an auditing and predicting tool in order to forecast building energy consumption. The Administration Building of the University of Sao Paulo is used as a case study. The building energy consumption profiles are collected as well as the campus meteorological data. Results show that both models are suitable for energy consumption forecast. Additionally, a parametric analysis is carried out for the considered building on EnergyPlus in order to evaluate the influence of several parameters such as the building profile occupation and weather data on such forecasting. (C) 2008 Elsevier B.V. All rights reserved.

On Building a Memory Evolutive System for Application to Learning and Cognition Modeling

We address here aspects of the implementation of a memory evolutive system (MES), based on the model proposed by A. Ehresmann and J. Vanbremeersch (2007), by means of a simulated network of spiking neurons with time dependent plasticity. We point out the advantages and challenges of applying category theory for the representation of cognition, by using the MES architecture. Then we discuss the issues concerning the minimum requirements that an artificial neural network (ANN) should fulfill in order that it would be capable of expressing the categories and mappings between them, underlying the MES. We conclude that a pulsed ANN based on Izhikevich`s formal neuron with STDP (spike time-dependent plasticity) has sufficient dynamical properties to achieve these requirements, provided it can cope with the topological requirements. Finally, we present some perspectives of future research concerning the proposed ANN topology.

West elevation, Neville Bonner Building, Brisbane

Riverside Expressway building, Expressway on right.

West elevation, Neville Bonner Building, Brisbane

William St building, facing Riverside Expressway. Landscaped courtyard space in foreground.

West elevation, Neville Bonner Building, Brisbane

William St building, facing Riverside Expressway.

Sunshading fins, Neville Bonner Building, Brisbane

Steel shading structure to East elevation of Riverside Expressway building. William St building and main entry area in background.

Windows, Neville Bonner Building, Brisbane

North elevation, Riverside Expressway building.

Facade articulation, Neville Bonner Building, Brisbane

Detail of precast concrete sunshading panels to freeway (West) elevation.

South elevation, Neville Bonner Building, Brisbane

William St building, as seen from across Riverside Expressway off-ramp.

Main entry, William St, Neville Bonner Building, Brisbane

William St building-Riverside Expressway building junction.

Facade articulation, Neville Bonner Building, Brisbane

Detail of precast concrete sunshading panels to freeway (West) elevation.

North elevation, Neville Bonner Building, Brisbane

As seen from Queens Wharf Road.

Entrance, Agriculture and Entomology Building, Brisbane

View to entrance.

Stair, Agriculture and Entomology Building, Brisbane

View to circulation stair from exterior.