Model Predictive Control (MPC) is a model-based technique widely and successfully used over the past years to improve control systems performance. A key factor prohibiting the widespread adoption of MPC for complex systems such as buildings is related to the difficulties (cost, time and effort) associated with the identification of a predictive model of a building. To overcome this problem, we introduce a novel idea for predictive control based on historical building data leveraging machine learning algorithms like regression trees and random forests. We call this approach Data-driven model Predictive Control (DPC), and we apply it to three different case studies to demonstrate its performance, scalability, and robustness. In the first case study we consider a benchmark MPC controller using a bilinear building model, then we apply DPC to a data-set simulated from such bilinear model and derive a controller based only on the data. Our results demonstrate that DPC can provide comparable performance with respect to MPC applied to a perfectly known mathematical model. In the second case study, we apply DPC to a 6 story 22 zone building model in EnergyPlus, for which model-based control is not economical and practical due to extreme complexity, and address a Demand Response problem. Our results demonstrate scalability and efficiency of DPC showing that DPC provides the desired power curtailment with an average error of 3%. In the third case study, we implement and test DPC on real data from an off-grid house located in L' Aquila, Italy. We compare the total amount of energy saved with respect to the classical bang-bang controller, showing that we can perform an energy saving up to 49.2%. Our results demonstrate the robustness of our method to uncertainties both in real data acquisition and weather forecast.

The large majority of the building heat losses occur through the building. Hence, the accurate evaluation of energy leakages, quantified by the thermal transmittance (U-value), is necessary, especially for energy labelling or city energy planning purposes, to foresee proper retrofit intervention and energy strategies. Among the techniques for the U-value assessment, the one that employs the quantitative infrared thermography (IRT) has spread in the last years, thanks to the possibility of easing the abovementioned processes due to reliable results, fast inspection, measurement carried out on large areas. However, a work that collects all the available techniques, explaining their weak and strength points, together with analogies and differences among the literature experiences, and which focuses on IRT, has not been carried out until now. This study starts from the common approaches for the U-value evaluation (analogies with coeval buildings, the calculation method, the in-situ measurements and the laboratory tests), with the underlying standard procedures and the most important advantages, problems, and potential sources of errors defined by the literature. Then, the IRT technique, and its development through the years, is detailed and discussed, focusing on analogies and differences among the available literature sources. Also, several recurring energy related problems, such as the detection and estimation of thermal bridging as well as the assessment of the ε-value of building materials, are shown. Finally, the qualification of IRT personnel and the perspectives in the building sector are briefly explained, to remark the need for specialized thermographers who deal with an ever evolving methodology.

Urban morphology and increasing building density play a key role in the overall use of energy and promotion of environmental sustainability. The urban environment causes a local increase of temperature, a phenomenon known as Urban Heat Island (UHI). The purpose of this work is the study of the possible formation of an UHI and the evaluation of its magnitude, in the context of a small city, carried out with the ENVI-met ® software. For this purpose, a simulation was needed, and this simulation is preparatory for a monitoring campaign on site, which will be held in the immediate future. ENVI-met areas and open zones with plenty of vegetation. These gradients arise in a really tiny space (few hundreds of meters), showing that the influence of urban geometry can be decisive in the characterization of local microclimate. Simulations, carried out considering the application of green or cool roofs, showed small relevant effects as they become evident only in large areas heavily built up (metropolis) subject to more intense climate conditions.

Flattened Gaussian beams are characterized by a waist profile that passes in a continuous way from a nearly flat illuminated region to darkness. The steepness of the transition region is controlled by an integer parameter N representing the order of the beam. Being expressible as a sum of N Laguerre-Gauss modes, a flattened Gaussian beam turns out to be very simple to study as far as propagation is concerned. We investigate the main features of the field distribution pertaining to a flattened Gaussian beam throughout the space and present experimental results relating to the laboratory production of this type of beam. (C) 1996 Optical Society of Americ

No abstract