• Energy Research

Predicting power demand of building heating systems is a challenging task due to the high variability of their energy profiles. Power demand is characterized by different heating cycles including sequences of various transient and steady-state phases. To effectively perform the predictive task by exploiting the huge amount of fine-grained energy-related data collected through Internet of Things (IoT) devices, innovative and scalable solutions should be devised. This paper presents PHi-CiB, a scalable full-stack distributed engine, addressing all tasks from energy-related data collection, to their integration, storage, analysis, and modeling. Heterogeneous data measurements (e.g., power consumption in buildings, meteorological conditions) are collected through multiple hardware (e.g., IoT devices) and software (e.g., web services) entities. Such data are integrated and analyzed to predict the average power demand of each building for different time horizons. First, the transient and steady-state phases characterizing the heating cycle of each building are automatically identified; then the power-level forecasting is performed for each phase. To this aim, PHi-CiB relies on a pipeline of three algorithms: the Exponentially Weighted Moving Average, the Multivariate Adaptive Regression Spline, and the Linear Regression with Stochastic Gradient Descent. PHi-CiB’s current implementation exploits Apache Spark and MongoDB and supports parallel and scalable processing and analytical tasks. Experimental results, performed on energy-related data collected in a real-world system show the effectiveness of PHi-CiB in predicting heating power consumption of buildings with a limited prediction error and an optimal horizontal scalability.

Nowadays, buildings are responsible of a large consumption of energy in our cities. Moreover, buildings can be seen as the smallest entity of urban energy systems. On these premises, in this paper we present a flexible and distributed co-simulation platform that exploits a multi-modelling approach to simulate and evaluate energy performance in smart buildings. The developed platform exploits the Mosaik co-simulation framework and implements the Functional Mock-up Interface (FMI) standard in order to couple and synchronise heterogeneous simulators and models. The platform integrates: i) the thermal performance of the building simulated with EnergyPlus, ii) the space heating and hot water system modelled as an heat pump with PID control strategy in Modelica, and iii) different Python models used to simulate household occupancy, electrical loads, roof-top photovoltaic production and smart meters. The platform guaranties a plug-and-play integration of models and simulators, hence, one or more models can be easily replaced without affecting the whole simulation engine. Finally, we present a demonstration example to test the functionalities and capabilities of the developed platform, and discuss future developments of our framework.

Following the Smart Grid view, current energy generation systems based on fossil fuels will be replaced with renewable energy sources. Photovoltaic (PV) is currently considered the most promising technology, due to decreasing costs of the devices and to the limited invasiveness in existing infrastructures, that make PV installations quite common urban buildings' roofs. To maximise both power production and Return Of Investment (ROI) of PV installations, new techniques and methodologies should be applied to limit sources of inefficiencies, like shading and power losses due to an incorrect installation. In this paper, we propose a novel solution for an optimal configuration and placement of PV systems in buildings' roofs. Given a number of alternative configurations and a roof of interest, it combines detailed geographic and irradiance information to determine the optimal PV installation, by maximizing both power production and ROI. Our simulation results on two real-world roofs demonstrate an improvement on power generation up to 23% w.r.t. standard compact installations. These results also highlight that a cost analysis, often ignored by standard installation strategies, is nonetheless necessary to guarantee optimal results in terms of PV production and revenue.

Sustainable energy, grids and networks (2017). doi:10.1016/j.segan.2017.08.001 Published by Elsevier, Amsterdam [u.a.]

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.

It is generally recognized that our behaviours affect the environment. However, it is difficult to correlate behaviour of an individual person to large-scale problems. This is usually due to insufficient ergonomy of available tools. The main cause is that most of user-awareness tools available are technology-centered instead of user-centered. In this paper, we present a participatory design approach we followed to design and develop an energy-aware mobile application for user-awareness on energy consumption for Smart Home monitoring. To engage end-users from the early design stages, we conduct two on-line surveys and a focus group involving about 630 people. Results allowed on identifying functional requirements and guidelines for mobile app design. The purpose of this research is to increase user-awareness on energy consumption using tools and methods required by users themselves. Furthermore in this paper, we present the technological choices that drove our implementation of an energy-aware application based on prosumers' requirements.

The aim of this paper is to form an analytical and critical framework to consider the uses of digital platforms in heritage field and practices and to provide methodologies for user profiling based on the identification of local stakeholders and their needs. Within the context of the EU H2020 research project RURITAGE, a resource ecosystem (RRE) of various integrated tools was created for shaping and addressing heritage-led knowledge and bottom-up strategies of local regeneration. More specifically, the RRE was conceived to provide local stakeholders with new methodologies and user-friendly tools based on bottom-up processes for identifying and actioning heritage and territorial features and turning these cultural natural values—as well as the gaps—into opportunities. This paper undertakes a comparative analysis of the integration of tools in other digital platforms for heritage practices and/or regeneration processes to explore the holistic approach to heritage knowledge and the effectiveness in engaging local stakeholders. In addition, it frames methodologies for local stakeholder and related needs identification. Through this comparative analysis among digital heritage platforms and through user profiling to target the needs of users by using the RRE as a case study, the paper explores the challenge of helping communities to shape a local heritage-led collaborative knowledge supported by integrated and user-friendly digital tools and to activate them in preserving and exploiting their territories and building shared and plural cultural heritage understandings, considering cultural heritage as a social need.