Livelli ottimali in funzione dei costi dei requisiti energetici ed edifici di riferimento

In ambito europeo i principi relativi al miglioramento della prestazione energetica degli edifici sono definiti nella direttiva 2002/91/UE (EPBD), così come riformulata dalla direttiva 2012/31/UE (EPBD recast). Tra i vari chiarimenti e prescrizioni, la EPBD recast ha introdotto a livello nazionale, un meccanismo di analisi comparativa con il proposito di determinare livelli ottimali di costo da utilizzare come metro per la formulazione di prescrizioni energetiche in ambito edilizio. L'articolo è incentrato sull'esposizione e sulla metodologia di applicazione da parte degli Stati Membri del quadro metodologico comparativo per il calcolo dei livelli ottimali in funzione dei costi per i requisiti minimi di prestazione energetica degli edifici e degli elementi edilizi, così come formulato dal Regolamento delegato (UE) N. 244/2012 e dalle linee guida (Orientamento della Commissione del 19 aprile 2012) ad esso associate, entrambi documenti emanati in ausilio all'applicazione della EPBD recast. L'articolo espone infine il caso italiano, delineando l'attività svolta da parte del gruppo di lavoro tecnico formato da CTI, ENEA ed RSE istituito presso il Ministero dello Sviluppo Economico, per l'attuazione della Direttiva 31/2010/EU: sulla base di quanto stabilito dalla Commissione Europea, tale gruppo di lavoro si sta adoperando per la definizione della metodologia comparativa da applicare ad edifici di riferimento su scala nazionale, al fine dell'ottenimento dei suddetti cost-optimal level

Improving summer energy performance of highly insulated buildings through the application of a thermal analysis by numerical simulation

The work presented in this paper is aimed at deepening the optimisation of the energy performance of highly insulated buildings in summer conditions through the application of an original methodology of thermal analysis. The methodology, already presented in a previous work (Ballarini et al., 2011), allows us to investigate the building energy balance and identify the most important parameters affecting the energy performance under certain conditions. The analysis is developed through the application of a dynamic simulation tool (EnergyPlus). The methodology consists of analysing the different contributions to the convective energy balance on internal air and their interrelations with different boundary conditions. Each contribution is split according to the dynamic driving forces of outdoor and indoor environment, i.e. external air temperature, solar radiation, internal air temperature and internal heat sources, and it is referred separately to the specific groups of components that exchange heat with internal air. This work focuses on the application of the above thermal analysis to a highly insulated single-family house in summer conditions, in two different Italian climatic zones. The methodology provides the mean values and the standard deviations of the contributions to the convective energy balance on internal air, and allows both to identify the main causes of low energy performance and to quantify the effects of possible retrofit or operational measures. As an exemplification, the effect of increasing the air change rate by natural ventilation during the night is investigated. The results show how the energy performance could be improved also in highly insulated buildings located in warm climate

On the 1-handles of the product V3XBn for a simply connected open 3-manifold V3

Although \pi_1^\inftyV^3 is an obstruction for killing stably the 1-handles of an open simply connected 3-manifold V^3, one can always get rid of the 1-handles of V^3\times B^n, for high enough n, at price of a certain nonmetrizable slackening of the topology

Improving summer energy performance of highly insulated buildings through the application of a thermal analysis by numerical simulation

The work presented in this paper is aimed at deepening the optimisation of the energy performance of highly insulated buildings in summer conditions through the application of an original methodology of thermal analysis. The methodology, already presented in a previous work (Ballarini et al., 2011), allows us to investigate the building energy balance and identify the most important parameters affecting the energy performance under certain conditions. The analysis is developed through the application of a dynamic simulation tool (EnergyPlus). The methodology consists of analysing the different contributions to the convective energy balance on internal air and their interrelations with different boundary conditions. Each contribution is split according to the dynamic driving forces of outdoor and indoor environment, i.e. external air temperature, solar radiation, internal air temperature and internal heat sources, and it is referred separately to the specific groups of components that exchange heat with internal air. This work focuses on the application of the above thermal analysis to a highly insulated single-family house in summer conditions, in two different Italian climatic zones. The methodology provides the mean values and the standard deviations of the contributions to the convective energy balance on internal air, and allows both to identify the main causes of low energy performance and to quantify the effects of possible retrofit or operational measures. As an exemplification, the effect of increasing the air change rate by natural ventilation during the night is investigated. The results show how the energy performance could be improved also in highly insulated buildings located in warm climates

Comment to the SEC in Support of the Enhanced Disclosure of Patent and Technology License Information

Intangible assets like IP constitute a large share of the value of firms, and the US economy generally. Accurate information on the intellectual property (IP) holdings and transactions of publicly-traded firms facilitates price discovery in the market and reduces transaction costs. While public understanding of the innovation economy has been expanded by a large stream of empirical research using patent data, and more recently trademark information this research is only as good as the accuracy and completeness of the data it builds upon. In contrast with information about patents and trademarks, good information about IP licensing is much less publicly available. Although IP royalties provide large in-bound trade flows to the United States, remarkably little is known about the economic realities of IP transactions. But not only are licensing royalties economically impactful, but building a better understanding of how markets for technology operate in a modern, innovation economy is important for the transparency of markets, and to the public and policy-makers. Open data on innovation is currently siloed, fragmented, and unfedeRrarated across a number of repositories (some electronic and others physical) including the Administrative Office of the Courts, Secretary of State Offices, Copyright Office, IRS, USPTO, SEC, FDA, NSF, SBA and others, raising search and discovery costs and undermining the goals of open data. Data on “comparables” tend to be thin in the industry, a situation that may offer a sub-optimal market environment for startup firms: these young entities often rely on selling intangibles, but have low bargaining power, and limited resources to invest in search and price discovery. Disclosures of material licenses and intellectual property information to the SEC addresses a number of existing gaps, with the potential to play an expanded role. In fact, IP license information is not widely available to the public through any other federal agency, even in cases where the IP was federally funded. Thus the IP license information available through the SEC is an invaluable resource to the public. One major limitation with the existing SEC licensing information, however, is that it is often difficult to find and manipulate. An impediment arises since the data are not tagged or designed to be easily combined with other information sources. One of us, for example, has sought to determine which firms have SEC-registered patent licenses over a period of time for the purpose of establishing a public database of licenses obtained through FOIA requests. However, there is no straightforward way for the public to search for this information, in the SEC record or otherwise. The overall thrust of our comments is to commend the SEC on the valuable disclosures its requirements encourage and to recommend preserving and augmenting, rather than diminishing them, in order to 1) produce more useful data and 2) reduce the costs of discovering and using existing data disclosed to the SEC. In many cases, an SEC requirement will not require reporting entities to create new information (e.g., when disclosing patents or licenses) but it will greatly reduce the costs to third parties of searching for this information

Comment to the SEC in Support of the Enhanced Disclosure of Patent and Technology License Information

Intangible assets like IP constitute a large share of the value of firms, and the US economy generally. Accurate information on the intellectual property (IP) holdings and transactions of publicly-traded firms facilitates price discovery in the market and reduces transaction costs. While public understanding of the innovation economy has been expanded by a large stream of empirical research using patent data, and more recently trademark information this research is only as good as the accuracy and completeness of the data it builds upon. In contrast with information about patents and trademarks, good information about IP licensing is much less publicly available. Although IP royalties provide large in-bound trade flows to the United States, remarkably little is known about the economic realities of IP transactions. But not only are licensing royalties economically impactful, but building a better understanding of how markets for technology operate in a modern, innovation economy is important for the transparency of markets, and to the public and policy-makers. Open data on innovation is currently siloed, fragmented, and unfedeRrarated across a number of repositories (some electronic and others physical) including the Administrative Office of the Courts, Secretary of State Offices, Copyright Office, IRS, USPTO, SEC, FDA, NSF, SBA and others, raising search and discovery costs and undermining the goals of open data. Data on “comparables” tend to be thin in the industry, a situation that may offer a sub-optimal market environment for startup firms: these young entities often rely on selling intangibles, but have low bargaining power, and limited resources to invest in search and price discovery. Disclosures of material licenses and intellectual property information to the SEC addresses a number of existing gaps, with the potential to play an expanded role. In fact, IP license information is not widely available to the public through any other federal agency, even in cases where the IP was federally funded. Thus the IP license information available through the SEC is an invaluable resource to the public. One major limitation with the existing SEC licensing information, however, is that it is often difficult to find and manipulate. An impediment arises since the data are not tagged or designed to be easily combined with other information sources. One of us, for example, has sought to determine which firms have SEC-registered patent licenses over a period of time for the purpose of establishing a public database of licenses obtained through FOIA requests. However, there is no straightforward way for the public to search for this information, in the SEC record or otherwise. The overall thrust of our comments is to commend the SEC on the valuable disclosures its requirements encourage and to recommend preserving and augmenting, rather than diminishing them, in order to 1) produce more useful data and 2) reduce the costs of discovering and using existing data disclosed to the SEC. In many cases, an SEC requirement will not require reporting entities to create new information (e.g., when disclosing patents or licenses) but it will greatly reduce the costs to third parties of searching for this information

Building high-level features using large scale unsupervised learning

We consider the problem of building high-level, class-specific feature detectors from only unlabeled data. For example, is it possible to learn a face detector using only unlabeled images? To answer this, we train a 9-layered locally connected sparse autoencoder with pooling and local contrast normalization on a large dataset of images (the model has 1 billion connections, the dataset has 10 million 200x200 pixel images downloaded from the Internet). We train this network using model parallelism and asynchronous SGD on a cluster with 1,000 machines (16,000 cores) for three days. Contrary to what appears to be a widely-held intuition, our experimental results reveal that it is possible to train a face detector without having to label images as containing a face or not. Control experiments show that this feature detector is robust not only to translation but also to scaling and out-of-plane rotation. We also find that the same network is sensitive to other high-level concepts such as cat faces and human bodies. Starting with these learned features, we trained our network to obtain 15.8% accuracy in recognizing 20,000 object categories from ImageNet, a leap of 70% relative improvement over the previous state-of-the-art