Publications

Authors: Giacomo Chiesa, Andrea Avignone, Tommaso Carluccio
University: Department of Architecture and Design, Politecnico di Torino, Italy

Smart building issues are critical for current energy and comfort managing aspects in built environments. Nevertheless, the diffusion of smart monitoring solutions via user-friendly graphical interfaces is still an ongoing issue subject to the need to diffuse a smart building culture and a low-cost series of solutions. This paper proposes a new low-cost IoT sensor network, exploiting Raspberry Pi and Arduino platforms, for collecting real-time data and evaluating specific thermal comfort indicators (PMV and PPD). The overall architecture was accordingly designed, including the hardware setup, the back-end and the Android user interface. Eventually, three distinct prototyping platforms were deployed for initial testing of the general system, and we analysed the obtained results for different building typologies and seasonal periods, based on collected data and users’ preferences. This work is part of a large educational and citizen science activity.

Authors: Giacomo Chiesa, Francesca Fasano, Paolo Grasso
University: Department of Architecture and Design, Politecnico di Torino, Italy

Energy consumption for space cooling is characterized by a continuously rising trend. In parallel, the number of installed domestic cooling units is significantly growing, as confirmed by International Energy Agency (IEA) documents and by yearly reports of sector-specific companies. The global penetration of the air conditioning market is, in fact, quickly increasing, especially in Asia, but also in the Americas and in Europe. This trend is connected to several causes, including climate change, urban heat islands, comfort culture, and building design choices that are inconsistent with respect to local climate. It is hence evident that alternative solutions for cooling, when environmental conditions are favourable, may be adopted and developed in order to reduce cooling energy needs and consequent GHGs (greenhouse gas emissions). Among low-energy alternatives, ventilative cooling (VC) is a valuable technique to reduce energy needs and consumption in buildings supporting free-running and/or fan-assisted ventilation for space cooling. This technique was demonstrated to be very effective in reducing overheating risks, but also to guarantee thermal and IAQ (indoor air quality) comfort in buildings during the summer season. However, the ventilative cooling potential occurs when external air temperatures are below comfort thresholds; therefore, its applicability is local and time-specific and is connected to local climate/weather conditions. As underlined for the majority of passive and low-energy cooling solutions, the non-homogeneous specific local potential has limited the current applications of VC with respect to passive heating technologies, that is sunspaces. Nevertheless, a consistent need to support the diffusion of VC solutions is evident, given this approach is not sufficiently covered and valorised by current regulations – see, for example, the recent analysis reported in – even in those climates in which it may support thermal comfort without cooling system activations during the majority of hours. This requires the development of methodologies to calculate the potential of ventilative cooling – see, for example, the results of the IEA EBC ANNEX 62 on Ventilative Cooling and other references supporting the widespread of VC.

Authors: Daniel Leiria, Hicham Johra, Anna Marszal-Pomianowska, Michal Zbigniew Pomianowski
University: Department of the Built Environment, Aalborg University, Denmark

This article presents a new methodology to disaggregate the energy demand for space heating (SH) and domestic hot water (DHW) production from single hourly smart heat meters installed in Denmark. The new approach is idealized to be easily applied to several building typologies without the necessity of in-depth knowledge regarding the dwellings and their occupants. This paper introduces, tests, and compares several algorithms to separate and estimate the SH and DHW demand. To validate the presented methodology, a dataset of 28 Danish apartments with detailed energy monitoring (separated SH and DHW usage) is used. The comparison shows that the best method to identify energy demand data points corresponding to DHW production events is the so-called “maximum peaks” approach. Furthermore, the best algorithm to estimate the SH and DHW separately is a combination of two methods: the Kalman filter and the Support Vector Regression (SVR). This new methodology outperforms the current Danish compliances typically used to estimate the annual DHW usage in residential buildings.

Authors: Juha Koskela, Pertti Järventausta
University: Unit of Electrical Engineering, Tampere University, Finland

Distributed electric power production by small-scale customers is increasing continuously. Photovoltaic production is a popular method of producing self-energy for customers. Additionally, power systems require more flexibility when weather-dependent renewable energy production increases. Small-scale customers can increase the self-consumption of self-produced energy by using batteries or a demand response operation. However, batteries require high investment, and demand response operations induce a loss of comfort. Customers who heat their buildings using electric heaters are a good target for demand response operations because their heating can be controlled with limited changes in the indoor temperature. The demand response potential of a building can be defined by simply using customer load profiles and knowledge of the outdoor temperature. Any other information is not required in the proposed novel method. A tolerable variation in indoor temperature corresponds to considerably smaller battery capacity, though it is still a significant amount. With an optimally sized photovoltaic system, it is possible to use both methods simultaneously to increase self-consumption. Maximal benefits can be attained from both methods if the battery system is used as a primary control and the demand response is used as a secondary control. The defined novel method for determining the demand response potential of small-scale customers can also be used when estimating the flexibility of a large customer group. Small-scale customers together can provide significant flexible capacity when their electrical heating is centrally controlled.

Authors: Juha Koskela, Antti Rautiainen, Pertti Järventausta
University: Tampere University, Finland

The popularity of small-scale residential energy production using photovoltaic power generation is predicted to increase. Self-production of electricity for self-consumption has become profitable mainly because of high-distribution costs and taxes imposed by the service providers on commercially produced electricity or because of the subsidies which reduce installation costs. Electrical energy storage can be used to increase the self-consumption potential of photovoltaic power. Additionally, electrical energy storage can lead to other benefits such as demand response or avoiding high load peaks. In this study, the profitability and sizing of a photovoltaic system with an associated electrical energy storage are analyzed from an economic perspective. The novel theory of sizing for profitability is presented and demonstrated using case studies of an apartment building and detached houses in Finland. To maximize the benefits, several alternative models for electricity metering and pricing are used and compared. The results demonstrated that the optimal size of the photovoltaic system could be increased by using electrical energy storage and suitable electricity pricing. This could lead to an increasing amount of photovoltaic production in the residential sector. Additionally, it is possible that when all the incentives are taken into account, electrical energy storage in combination with photovoltaic power generation would be more profitable than photovoltaic power generation alone. Photovoltaic power generation also increased the profitability of electrical energy storage, which could mean that the implementation of electrical energy storage in the residential sector could likewise increase.

Authors: Juha Koskela, Antti Mutanen, Pertti Järventausta
University: Unit of Electrical Engineering, Tampere University, Finland

The profitability of domestic battery energy storage systems has been poor and this is the main barrier to their general use. It is possible to increase profitability by using multiple control targets. Market price-based electricity contracts and power-based distribution tariffs alongside storage of surplus photovoltaic energy make it possible to have multiple control targets in domestic use. The battery control system needs accurate load forecasting so that its capacity can be utilized in
an optimally economical way. This study shows how the accuracy of short-term load forecasting affects cost savings by using batteries. The study was conducted by simulating actual customers’ load profiles with batteries utilized for different control targets. The results of the study show that knowledge of customers’ load profiles (i.e., when high and low peaks happen) is more important that actual forecast accuracy, as measured by error criteria. In many cases, the load forecast based on customers’ historical load data and the outdoor temperature is sufficient to be used in the control system, but in some cases a more accurate forecast can give better cost savings.

Authors: Nikos Dimitropoulos, Nikolaos Sofias, Panagiotis Kapsalis, Zoi Mylona, Vangelis Marinakis, Niccolo Primo, Haris Doukas
University: Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Greece

Photovoltaic (PV) modules and solar plants are one of the main drivers towards zero-carbon future. Energy communities that are engaging citizens through collective energy actions can reinforce positive social norms and support the energy transition. Furthermore, by incorporating Artificial Intelligence (AI) techniques, innovative applications can be developed with huge potential, such as supply and demand management, energy efficiency actions, grid operations and maintenance actions. In this context, the scope of this paper is to present an approach for forecasting an energy cooperative’s solar plant short term production by using its infrastructure and monitoring system. More specifically, four Machine Learning (ML) and Deep Learning (DL) algorithms are proposed and trained in an operational solar plant producing high accuracy short-term forecasts up to 6 hours. The results can be used for scheduling supply of the energy communities and set the base for more complex applications that require accurate short-term predictions, such as predictive maintenance.

Authors: May Zune, Maria Kolokotroni
University: College of Engineering, Design and Physical Sciences, Brunel University London, UK

Occupants in residential buildings usually control natural ventilation through window openings. However, few studies have developed simple rules based on the outdoor weather forecast that can inform the occupants to predict the indoor condition by applying natural ventilation for thermal comfort and indoor air quality (IAQ). This paper describes a model based on indoor/outdoor correlations, derived through simulations using EnergyPlus and CONTAM, to help occupants maintain internal environmental quality manually or through simple controls. Simulation test cases were defined considering factors that can statistically change correlations, including the effect of single-sided and cross-ventilation, trickle ventilators, different schedules for window opening, heating and occupancy, size of the model, and building orientation for the window opening. The study found strong correlations between external and internal hourly temperatures, as well as between airflow and wind speed, and the inverse temperature differences between outdoor and indoors. The derived model consists of coefficients of determination (R2) between the correlated parameters and a set of equations to calculate thermal comfort and pollutant concentrations in the space. The derived correlations are then used independently to predict internal operative temperature and ventilation rates. Based on these parameters, thermal comfort is evaluated for the next period (hours or days) to predict overheating (based on the adaptive thermal comfort model) and indoor concentrations using contaminant mass balance equations for indoor CO2 concentration. An example of the application of this model is presented for a location in central Europe where a pilot building of the PRELUDE H2020 project is located. The findings of this study indicate how to reduce a large amount of data down to a manageable form, useful for occupants to identify indoor conditions for their space based on climatic conditions. This study highlights the importance of a user-driven decision-making process for predicting the indoor conditions from outdoor climatic parameters which could encourage behavioural change strategies and effective use of natural ventilation for thermal comfort and IAQ.

Authors: Daniel Leiria, Hicham Johra, Evangelos Belias2, Davide Quaggiotto, Angelo Zarrella, Anna Marszal-Pomianowska, Michal Pomianowski
University: Department of the Built Environment, Aalborg University, Denmark

One of the initiatives to reach the European decarbonization goal is the roll-out of smart heating meters in the building stock. However, these meters often record the total energy usage with only hourly resolution, without distinguishing between space heating (SH) and domestic hot water (DHW) production. To tackle this limitation, this paper presents the validation of a new methodology to estimate the SH and DHW from total measurements in different building types in three countries (Denmark, Switzerland, and Italy). The method employs a combined smoothing algorithm with a support vector regression (SVR) to estimate the different heating uses. The estimation results are compared with the different countries’ DHW compliance calculations. The comparison showed that the compliance calculations outperformed this method by considering the validation dataset characteristics.

Authors: M. Zune, M. Kolokotroni 

In CATE2022, RESILIENT COMFORT: Climate Change, COVID and Ventilation, 5th-6th September 2022, Edinburgh UK