Machine learning for estimation of building energy consumption and performance:a review

Ever growing population and progressive municipal business demands for constructing new buildings are known as the foremost contributor to greenhouse gasses. Therefore, improvement of energy eciency of the building sector has become an essential target to reduce the amount of gas emission as well as fossil fuel consumption. One most eective approach to reducing CO2 emission and energy consumption with regards to new buildings is to consider energy eciency at a very early design stage. On the other hand, ecient energy management and smart refurbishments can enhance energy performance of the existing stock. All these solutions entail accurate energy prediction for optimal decision making. In recent years, articial intelligence (AI) in general and machine learning (ML) techniques in specic terms have been proposed for forecasting of building energy consumption and performance. This paperprovides a substantial review on the four main ML approaches including articial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance

The effect of different oxide layers on the sensing properties of anodic alumina nanoporous film

In the present work, anodized aluminum oxide template was prepared by accelerated mild anodization technique in 0.6M phosphoric aside and 175 V, anodization voltage. Pore widening was performed by chemical etching in 0.5M phosphoric acid for 8, 16, 32, 40 minutes. Scanning Electron Microscopy (SEM) images showed the pores, diameter exponentially increases with etching time. By depositing silver contacts on the prepared samples and using an RC circuit for applying impedance spectroscopy, the characteristics of the humidity sensor based on constructed samples were investigated. The maximum response was seen for the sample etched for 40 minutes. For this sample, the detectable threshold of relative moisture was 30% and the response and the recovery time were 8, 2 seconds, respectivel

On integral input-to-state stability for a feedback interconnection of parameterised discrete-time systems

This paper addresses integral input-to-state stability (iISS) for a feedback interconnection of parameterised discrete-time systems involving two subsystems. Particularly, we give a construction for a smooth iISS Lyapunov function for the whole system from the sum of nonlinearly weighted Lyapunov functions of individual subsystems. Motivations for such a construction are given. We consider two main cases. The first one investigates iISS for the whole system when both subsystems are iISS. The second one gives iISS for the interconnected system when one of subsystems is allowed to be input-to-state stable. The approach is also valid for both discrete-time cascades and a feedback interconnection of iISS and static systems. Examples are given to illustrate the effectiveness of the results