Human daily activity behavioural clustering from Time Use Survey

Identification of daily pattern behaviours of people from Time use Survey with the purpose of defining archetypes of persons is becoming a new rising research field. Identified patters are useful for developing more realistic models to simulate activities of citizens related to mobility and households energy consumption. These models are required to test and develop simulation scenarios of future smart grids and cities. In this work we apply the k-modes algorithm to clusterize the Italian TUS data-set. For the best of our knowledge this is the only study that applied unsupervised clusterization and classification of Italian TUS data and the only one that extended the analysis to mobility activities of the TUS data-sets. From experimental results we obtained different clusters for weekdays, saturdays and holidays, respectivel

When Industry 4.0 meets World Class Manufacturing: Developing a Smart Digital Retrofitting strategy for sustainable manufacturing operations

This paper develops an Industry 4.0 step-by-step implementation strategy drawing from the World Class Manufacturing methodology to achieve sustainable value creation in manufacturing operations. In particular, we focus on the first step of such a strategy – namely Smart Digital Retrofitting – a brownfield approach that integrates new technologies and sensors into legacy systems to achieve Industry 4.0 basic requirements of seamless communication, interoperability among machines, and real-time capability. We characterize this step with the socio-technical system factors, describe its positive outcomes in terms of productivity gains and waste and losses reduction, and discuss its theoretical and practical contributions

A calibration facility for automatic weather stations

Environmental monitoring by automatic weather stations (AWSs) is growing as a result of the increasing number and reliability of surface observations. In order to ensure data traceability and obtain more comprehensive data on the performance of AWSs, a new transportable calibration facility was manufactured at the Italian Institute of Metrology (INRiM) in the framework of the project MeteoMet. The facility is equipped with temperature and pressure reference sensors directly traceable to the International System of Units (SI) to obtain meteorological data with well-documented calibration uncertainty. In this calibration system, temperature and pressure can be controlled simultaneously and independently so that all combinations over the ranges are possible. The nominal ranges are: absolute pressure 50 to 110 kPa and temperature -25 to 50 degrees C. The availability of a large range of atmospheric variability and the possibility of studying the mutual influence effects on sensors response are important characteristics of the facility. This apparatus is also designed to permit a control in humidity, in order to complete the characterization of the whole AWS pressure-temperature-humidity modulus. As a matter of fact, the final version of the facility will be equipped with a humidity generator for hygrometer calibrations. Finally, the calibration system is designed to be transportable, therefore allowing the calibration of AWSs located at sites that are difficult to access such as Ny-angstrom lesund (Svalbard) stations, where the facility was employed. The design and the technical characteristics are reported in this paper

An Hybrid Model-Free Reinforcement Learning Approach for HVAC Control

Traditional Heating Ventilation and Air Conditioning (HVAC) systems are extremely energy draining appliances, and their use is ever increasing with urbanisation. For this reason, strong research effort has been put in the development of novel control strategies for the optimal management of HVAC systems, aiming at reducing energy consumption without affecting thermal comfort. In this paper, we propose an hybrid model-free Reinforcement Learning approach for HVAC control able to optimise both energy consumption or users comfort. Our methodology is compared with two baseline solutions in literature based on an EnergyPlus controller and a Model Predictive Control. Results show that our methodology can outperform both baselines in terms of energy consumption reduction or thermal comfort optimisation, given that either of the two objectives is appropriately chosen during the training and the hyperparameters selection phase

Arctic metrology: calibration of radiosondes ground check sensors in Ny-Ålesund

The Arctic research village of Ny-Ålesund (79 ° N, 12 ° E) on the Spitsbergen island of Svalbard archipelago, with its logistics and infrastructure, provides a unique access to the Arctic environment. Among the several international environmental and climate monitoring programmes constantly running there, the Global Climate Observing System (GCOS) Reference Upper Air Network (GRUAN) has established one of its stations at the Alfred Wegener Institute observatory. Calibration of sensors and measurement traceability are fundamental aspects of climate observations, as requested by the GRUAN measurement procedures. In the framework of the MeteoMet project, a transportable climatic chamber for the calibrations of air temperature and pressure sensors was studied and manufactured. In June 2014, a calibration campaign involved the transport and use of one of those systems in the Ny-Ålesund GRUAN station. The result of the campaign has been the complete calibration of temperature and pressure sensors for radiosonde pre-launch ground checks. The resulting calibration curves were obtained with lower uncertainties and more robust characterization of the sensors, with respect to the usual procedures adopted. Given the opportunity of the calibration device operating already in place and the presence of the metrology staff, the calibration was extended to the sensors equipping the Amundsen-Nobile Climate Change Tower. The present study reports on the ‘Arctic metrology 2014’ campaign and the plans for the establishment of a permanent laboratory for metrology in Ny-Ålesund. The aim is to address the measurement traceability needs arising from the multidisciplinary measurements made by the scientific community operating there

Effectiveness of neural networks and transfer learning to forecast photovoltaic power production

Artificial Neural Networks (ANNs) can successfully be integrated into smart models for energy prediction, but require large datasets for training. This investigation presents an innovative methodology for photovoltaic power generation forecasting with ANNs, when only a limited amount of real data is available, and has been tested and validated on a real-life photovoltaic installation. Feature selection identifies which meteorological features most impact photovoltaic power generation. A simulator, which accurately models a real photovoltaic installation, is used to create an artificial, but accurate and realistic, dataset of power generation large enough to effectively train and test different ANNs. These are then exploited on a portion of real, but limited, dataset of power generated by the real photovoltaic installation on which the simulator is modeled. Finally, different transfer learning techniques are used to tune the ANN models with the remaining portion of the real, but limited, dataset of photovoltaic power generation

Effectiveness of neural networks and transfer learning for indoor air-temperature forecasting

Starting in 2007, EU set energy efficiency improvement targets in sectors with high energy-saving potential such as buildings. ICT allows innovative opportunities for energy consumption forecast to integrate with new control policies such as Demand/Response and Demand Side Management to reduce energy waste. However, such technologies must overcome challenges such as the lack of accurate historic data required for predictions. This article proposes an innovative methodology supporting the energy management of HVAC systems, through Smart Building indoor air-temperature forecast. The applicability of innovative neural networks for time-series predictions is explored. These neural networks are first trained on an artificial but realistic dataset based on BIM simulations with real meteorological data. The inference phase is then carried out on a second dataset collected by IoT devices. Finally, Transfer Learning techniques are exploited to improve the performances predictions. Fanger’s model is applied to validate results, showing consistent levels of accuracy and comfort

Data-driven predictive maintenance:a methodology primer

Predictive maintenance aims at proactively assessing the current condition of assets and performing maintenance activities if and when needed to preserve them in the optimal operational condition. This in turn may lead to a reduction of unexpected breakdowns and production stoppages as well as maintenance costs, ultimately resulting in reduced production costs. Empowered by recent advances in the fields of information and communication technologies and artificial intelligence, this chapter attempts to define the main operational blocks for predictive maintenance building upon existing standards and discuss key datadriven methodologies for predictive maintenance. In addition, technical information related to potential data models for storing and communicating key information are provided, finally closing the chapter with different deployment strategies for predictive analytics as well as identifying open issues

Combining BIM, GIS, and IoT to Foster Energy Management and Simulation in Smart Cities

This chapter presents a novel distributed software infrastructure to enable energy management and simulation of novel control strategies in smart cities. In this context, the following heterogeneous information, describing the different entities in a city, needs to be taken into account to form a unified district information model: internet-of-things (IoT) devices, building information model, system information model, and georeferenced information system. IoT devices are crucial to monitor in (near-) real-time both building energy trends and environmental data. Thus, the proposed solution fulfills the integration and interoperability of such data sources providing also a correlation among them. Such correlation is the key feature to unlock management and simulation of novel energy policies aimed at optimizing the energy usage accounting also for its impact on building comfort. The platform has been deployed in a real-world district and a novel control policy for the heating distribution network has been developed and tested. Finally, experimental results are presented and discusse