Building thermal dynamics modeling with deep learning exploiting large residential smart thermostat dataset

In this paper, we present a deep learning approach to model building thermal dynamics with large-scale smart thermostat data collected from residential buildings. We developed a Long Short-Term Memory (LSTM) model as a baseline and compared it to a CNN-LSTM model to predict indoor air temperature in a multi-step time horizon in 164 buildings. The study showed that the proposed CNN-LSTM achieved an average of 0.26 °C Mean Absolute Error (MAE) for one-hour-ahead (12 future steps) predictions, which is over 6% of improvement comparing with the baseline. Furthermore, the results indicated that the CNN-LSTM models achieved more robust performance across different building characteristics, system configurations and locations, with a standard deviation reduction of 22%, proving the effectiveness and generalizability of the proposed approach

Thermal Metrics for Data Centers: A Critical Review

Thermal management and air distribution performance are assuming a key role for achieving the energy saving and the IT equipment reliability for data centers (DCs). In recent years, to monitor and to control their variation several thermal performance metrics were introduced. This work presents a critical review on the most important thermal indices for DCs currently used. The main formulas and physical models on which they are based were discussed. Moreover, a critical analysis on the main advantages and drawbacks of each metric is carried out

New Avoparcin-like Molecules from the Avoparcin Producer Amycolatopsis coloradensis ATCC 53629

Amycolatopsis coloradensis ATCC 53629 is the producer of the glycopeptide antibiotic avoparcin. While setting up the production of the avoparcin complex, in view of its use as analytical standard, we uncovered the production of a to-date not described ristosamynil-avoparcin. Ristosamynil-avoparcin is produced together with α-and β-avoparcin (overall indicated as the avoparcin complex). Selection of one high producer morphological variant within the A. coloradensis population, together with the use of a new fermentation medium, allowed to increase productivity of the avoparcin complex up to 9 g/L in flask fermentations. The selected high producer displayed a non-spore forming phenotype. All the selected phenotypes, as well as the original unselected population, displayed invariably the ability to produce a complex rich in ristosamynil-avoparcin. This suggested that the original strain deposited was not conforming to the description or that long term storage of the lyovials has selected mutants from the original population

Transfer learning for smart buildings: A critical review of algorithms, applications, and future perspectives

Smart buildings play a crucial role toward decarbonizing society, as globally buildings emit about one-third of greenhouse gases. In the last few years, machine learning has achieved a notable momentum that, if properly harnessed, may unleash its potential for advanced analytics and control of smart buildings, enabling the technique to scale up for supporting the decarbonization of the building sector. In this perspective, transfer learning aims to improve the performance of a target learner exploiting knowledge in related environments. The present work provides a comprehensive overview of transfer learning applications in smart buildings, classifying and analyzing 77 papers according to their applications, algorithms, and adopted metrics. The study identified four main application areas of transfer learning: (1) building load prediction, (2) occupancy detection and activity recognition, (3) building dynamics modeling, and (4) energy systems control. Furthermore, the review highlighted the role of deep learning in transfer learning applications that has been used in more than half of the analyzed studies. The paper also discusses how to integrate transfer learning in a smart building's ecosystem, identifying, for each application area, the research gaps and guidelines for future research directions

Potentialities of a Low Temperature Solar Heating System Based on Slurry Phase Change Materials (PCS)

Flat-plate solar thermal collectors are the most common devices to convert solar energy into heat. Water-based fluids are commonly adopted as heat carrier for this technology, although their efficiency is limited by some thermodynamic and heat storage constraints. To overcome some of these limitations, an innovative approach is the use of latent heat, which can be available by means of microencapsulated slurry PCMs (mixtures of microencapsulated Phase Change Materials, water and surfactants). The viscosity of these fluids is similar to that of water and they can be easily pumped. In the present work, some of the thermo-physical and rheological properties and material behaviour that interest flat-plate solar thermal collectors with slurry PCM as the heat carrier fluid are analysed. Concepts of solar thermal systems filled with a slurry phase change material are proposed and a prototypal system is presented. Possible advantages and drawbacks of this technology are also discussed

Long-Term Safety and Usefulness of Mexiletine in a Large Cohort of Patients Affected by Non-dystrophic Myotonias

Objective: The aim of our study was to evaluate the long-term efficacy and safety of mexiletine in 112 patients affected by genetically confirmed non-dystrophic myotonias. The study was performed at the Neurophysiologic Division of Fondazione Policlinico Universitario A. Gemelli Istituto di Ricerca e Cura a Carattere Scientifico (IRCCS), Rome and the Children's Hospital Bambino Gesù, Rome. Methods: The treatment was accepted by 59 patients according to clinical severity, individual needs, and concerns about a chronic medication. Forty-three patients were affected by recessive congenita myotonia, 11 by sodium channel myotonia, and five by dominant congenital myotonia. They underwent clinical examination before and after starting therapy, and Electromyography (EMG). A number of recessive myotonia patients underwent a protocol of repetitive nerve stimulations, for detecting and quantifying the transitory weakness, and a modified version of the Timed Up and Go test, to document and quantify the gait impairment. Results: Treatment duration ranged from 1 month to 20 years and the daily dosages in adults ranged between 200 and 600 mg. No patient developed cardiac arrhythmias causing drug discontinuation. Mexiletine was suspended in 13 cases (22%); in three patients, affected by Sodium Channel myotonia, because flecainide showed better efficacy; in one patient because of a gastric cancer antecedent treatment; in four patients because of untreatable dyspepsia; and five patients considered the treatment not necessary. Conclusions: In our experience, mexiletine is very useful and not expensive. We did not observe any hazarding cardiac arrhythmias. Dyspepsia was the most frequent dose-limiting side effect

Data Mining for Thermal Analysis of Big Dataset of HPC-Data Center

Greening of Data Centers could be achieved through energy savings in two major areas namely: compute systems and cooling systems. A reliable cooling system is necessary to produce a persistent flow of cold air to cool the servers due to increasingly demanding computational load. Servers’ dissipated heat effects a strain on the cooling systems. Consequently, it is imperative to individual servers that frequently occur in the hotspot zones. This is facilitated through the application of data mining techniques to an available big data set with thermal characteristics of HPC-ENEA-Data Center, namely Cresco 6. This work involves the implementation of an advanced algorithm on the workload management platform produces hotspots maps with the goal to reduce data centre wide thermal-gradient, and cooling effectiveness

A potential beach monitoring based on integrated methods

This study focuses on the analysis of sandy beaches by integrating sedimentological, geomorphological, and geophysical investigations. The beach represents an extremely variable environment where different natural processes act simultaneously with human activities, leading to the gathering of different methodologies of the Earth Sciences to study its evolution in space and time. The aim of this research is to propose a potential procedure for monitoring the morpho-sedimentary processes of sandy beaches by analyzing the textural and compositional characteristics of the sands and quantifying the volumes involved in the coastal dynamics. The study area includes two Apulian sandy beaches (Torre Guaceto and Le Dune beach) that are representative of the coastal dynamics of a large sector of the central/northern Mediterranean Sea involving the southern Adriatic Sea and the northern Ionian Sea. Sedimentological and ecological investigations allowed to describe the textural and compositional characteristics of the beach sands by interpreting their sand provenance and the physical/biological interactions within the beach. The topographic surveys carried out with a Terrestrial Laser Scanner and an Optical Total Station, aimed to quantify the variations of sediment volume over time, whereas the Delft3d software was applied to analyze the effects of the dominant wave motion on the sedimentary dynamics. Lastly, the geophysical techniques which included Sub Bottom Profiler procedures, Ground Penetrating Radar investigation, and resistivity models enabled us to calculate the sand sediment thickness above the bedrock