Energy Use in Residential Buildings: Impact of Building Automation Control Systems on Energy Performance and Flexibility

Comprehensive energy demand and usage data for building automation

Buildings are essential in satisfying our daily need for comfort (privacy, protection from weather, etc.) and are responsible for almost half of the world's total energy consumption. Research at the interface of room comfort and energy efficiency is of critical societal importance. At the same time, there is a lack of publicly available data to optimize important building functions automatically. It is only through data-driven approaches that building automation becomes financially affordable and achieves widespread adoption. In this publication, measurement data from three buildings of the NEST platform are made publicly available. The dataset includes detailed information on energy consumption (electricity, heating, cooling, domestic hot water), building operation (set points, valve openings, windows), and occupant practice (e.g., presence, operation of blinds and kitchen, showering patterns). All data have been measured over four years and with a temporal resolution of 1 minute. This combination of information allows learning the function of different building types (office and residential) and thus addresses important research gaps.

An Analytical Model for the Impact of Building Control and Automation Upgrade on Space Heating Energy Efficiency

Intelligent building management systems are proven to lead to energy savings and are an integral component of smart buildings. The procedures developed in the EN standards describe the methodology for calculating the energy savings achieved by improving the automation and control levels of separate services in building systems. However, although this method is used in practice, it is rarely applied or investigated by the research community. Typically, energy savings resulting from a single automation improvement intervention in a building heating system are observed, while the holistic view of combined automation upgrades is not considered. Therefore, the purpose of this study was to assess the energy savings resulting from several upgrades to control levels in the heating system components of the building. In addition, this research provides a rationale for the impact of multiple automation and control options for heating systems as well as examines the difference in energy savings. Finally, an analytical model is developed and demonstrated to assess the feasibility of building automation and control upgrades, by determining the allowed investment according to a set of predefined indicators.

Sub-micron particle number emission from residential heating systems: A comparison between conventional and condensing boilers fueled by natural gas and liquid petroleum gas, and pellet stoves

Pollutant emissions from residential heating systems represent a main concern in terms of outdoor air quality. Differently from other pollutants, sub-micron particle emission from heating systems has not yet been exhaustively characterized by the scientific literature, with limited data available, in particular, for gas-fueled boilers. In the present paper, an experimental campaign to measure the sub-micron particle number concentrations and distributions at the stack of different automatically-fed small-scale heating systems (conventional and condensing boilers fueled by natural gas and liquid petroleum gas, and pellet stoves) was performed. Based on the measured concentrations, corresponding emission rates and emission factors were also estimated. The results of the experimental campaign revealed that the highest concentrations were measured for pellet stoves (median value >10⁷ part. m^-3), whereas conventional (about 1 × 10⁶ part. m^-3) and condensing boilers (<10⁶ part. m^-3) presented much lower concentrations. No effect of the fuel (natural gas, liquid petroleum gas) on the total concentration measured at the stack of boilers was recognized, whereas a smaller distribution mode (at 10 nm) was measured for gas-fired boilers. Because of the particle concentration values, the highest particle emission rates and factors were the pellet stove ones (median values of 2.1 × 10¹⁵ part. h^-1 and 8.4 × 10¹³ part. kWh^-1, respectively), whereas emission rates for conventional and condensing boilers were about 5 × 10¹³ part. h^-1 and 2 × 10¹³ part. h^-1, respectively. The estimated emission factors were also adopted to perform a simplified evaluation of the relative contributions of the investigated automatically-fed small-scale heating systems in terms of particle number on a national scale (Italy): we obtained that the pellet stove contribution is the main one as it accounts for 87% of total emissions of particle number for heating purpose.

A Hybrid Approach in Design of Building Energy Management System with Smart Readiness Indicator and Building as a Service Concept

Improving energy efficiency and increasing the level of intelligence are two main factors determining the current development trends for new and modernized buildings. They are especially important in the perspective of development of prosumer installations and local microgrids. A key tool to achieve these goals is a well-designed and implemented Building Automation and Control System (BACS). This paper presents a new hybrid approach to the design and technical organization of BACS based on the provisions of the EN 15232 standard and the guidelines of the Smart Readiness Indicator (SRI) defined in the Energy Performance of Buildings Directive 2018 (EPBD 2018). The main assumptions of this hybrid approach along with examples of functional BACS designs for small prosumer installations organized according to them are provided. Potential impact on building energy performance is discussed as well. Finally, a SWOT analysis of the possibility of merging the EN 15232 standard guidelines and the SRI assessment methodology to develop uniform technical guidelines for the BACS functions design and evaluation of their impact on the buildings’ energy efficiency are discussed.

Assessment of Building Automation and Control Systems in Danish Healthcare Facilities in the COVID-19 Era

A well-designed and properly operated building automation and control system (BACS) is key to attaining energy-efficient operation and optimal indoor conditions. In this study, three healthcare facilities of a different type, age, and use are considered as case studies to investigate the functionalities of BACS in providing optimal air quality and thermal comfort. IBACSA, the first-of-its-kind instrument for BACS assessment and smartness evaluation, is used to evaluate the current systems and their control functionalities. The BACS assessment is reported and analyzed. Then, three packages of improvements were implemented in the three cases, focusing on (1) technical systems enhancement, (2) indoor air quality and comfort, and (3) energy efficiency. It was found that the ventilation system domain is the best performer in the three considered cases with an overall score of 52%, 89% and 91% in Case A, B, and C, respectively. On the other hand, domestic hot water domain scores are relatively low, indicating that this is an area where Danish healthcare facilities need to provide more concentration on. A key finding indicated by the assessment performed is that the three buildings score relatively very low when it comes to the impact criteria of energy flexibility and storage.

AUSTRET: An Automated Step Response Testing Tool for Building Automation and Control Systems

Building energy consumption is still one of the main contributions to global carbon emissions. With the overall digitalization in the building sector, building automation and control systems (BACS) are to play a more important and key role in improving the building sector performance. A well-designed BACS at the building design phase with a high level of control functionalities is not a guarantee for efficient building operation and successful control and management strategies in the operational phase. Thus, a systematic automated initial and retro-commissioning process is key to test the performance of the automation system and the response of the integrated HVAC systems. This is an arduous and time-consuming task susceptible to human errors. As an alternative, the current study proposes a methodological framework to automate step response testing of BACS and to optimize the different steps of this process in a cost-effective way. In addition to newly built buildings, the framework can be applied in existing or retrofitted medium to large-sized buildings that have a building management system capable of receiving actuator commands and responsible to provide updates of several state variables. Based on the proposed framework, a first-of-its kind tool “AUSTRET” for building automated step response testing of BACS is designed and developed. The tool provides the necessary input configuring parameters, building system selection, and output results for each performed test. The framework aims to act upon ventilation, room heating and cooling, and water heating and cooling modules in a building. The implementation and demonstration of the AUSTRET in a medium-sized building case study for two different building systems are presented and evaluated: (1) Ventilation/fan, (2) Room heating. The results show the different dynamic responses on these two systems and how misleading input parameter configuration can invalidate step response tests. The preliminary results highlight the capability of using AUSTRET as a key component in both building initial and retro-commissioning applications.

Implementation and Simulation of Real Load Shifting Scenarios Based on a Flexibility Price Market Strategy—The Italian Residential Sector as a Case Study

This work aims to evaluate the Flexibility Potential that a residential household can effectively provide to the public grid for participating in a Demand Response activity. In detail, by using 14 dwellings electrical data collection, an algorithm to simulate the Load Shifting activity over the daytime is implemented. That algorithm is applied to different scenarios having considered the addition of several technical constraints on the end users’ devices. In such a way, more realistic demand-side management actions are implemented in order to assess the Flexibility Potential deriving from the loads shifting. Basically, by performing simulations it is possible to investigate how the household appliances real operating conditions can reduce the theoretical Flexibility Potential extent. Starting from a Flexibility Price-Market-based Strategy, this work simulates the shifting over the day and night-time of some flexible loads, i.e., the shiftable and the storable ones. Specifically, all instants where load curtailments and enhancements occur over the typical day, the flexibility strategy effectiveness in terms of percentage, the power and energy that are potentially flexible, are evaluated. All the simulations are performed only for residential consumers to evaluate the actual dwellings Flexibility Potential in the absence of any electrical storage and production systems. The outcomes of these simulations show an average Theoretical Flexibility reduction, which is calculated as the fraction of appliances’ cycles shifting over the total ones, equal to 53%, instead of 66%; in a single dwelling, a maximum variation equal to 29% has been registered. In the end, the monthly average shifted energy per dwellings decreases from 27 to 18 kWh, entailing 32.5% off.

Load Shifting and Peak Clipping for Reducing Energy Consumption in an Indian University Campus

This paper analyzes the intelligent use of time-varying electrical load via developing efficient energy utilization patterns using demand-side management (DSM) strategies. This approach helps distribution utilities decrease maximum demand and electrical energy billing costs. A case study of DSM implementation of electric energy utility for an educational building Alagappa Chettiar Government College of Engineering and Technology (ACGCET) campus was simulated. The new optimum energy load model was established for peak and off-peak periods from the system’s existing load profile using peak clipping and load shifting DSM techniques. The result reflects a significant reduction in maximum demand from 189 kW to 170 kW and a reduction in annual electricity billing cost from $11,340 to $10,200 (approximately 10%) in the upgraded system. This work highlights the importance of time of day (TOD) tariff structure consumers that aid reduction in their distribution system’s maximum demand and demand charges.

A Neural Network for Monitoring and Characterization of Buildings with Environmental Quality Management, Part 1: Verification under Steady State Conditions

Introducing integrated, automatic control to buildings operating with the environmental quality management (EQM) system, we found that existing energy models are not suitable for use in integrated control systems as they poorly represent the real time, interacting, and transient effects that occur under field conditions. We needed another high-precision estimator for energy efficiency and indoor environment and to this end we examined artificial neural networks (ANNs). This paper presents a road map for design and evaluation of ANN-based estimators of the given performance aspect in a complex interacting environment. It demonstrates that in creating a precise representation of a mathematical relationship one must evaluate the stability and fitness under randomly changing initial conditions. It also shows that ANN systems designed in this manner may have a high precision in characterizing the response of the building exposed to the variable outdoor climatic conditions. The absolute value of the relative errors ( M a x A R E ) being less than 1.4% for each stage of the ANN development proves that our objective of monitoring and EQM characterization can be reached.

How the Italian Residential Sector Could Contribute to Load Flexibility in Demand Response Activities: A Methodology for Residential Clustering and Developing a Flexibility Strategy

This work aims at exploring the potential contribution of the Italian residential sector in implementing load flexibility for Demand Response activities. In detail, by combining experimental and statistical approaches, a method to estimate the load profile of a dwelling cluster of 751 units has been presented. To do so, 14 dwelling archetypes have been defined and the algorithm to categorise the sample units has been built. Then, once the potential flexible loads for each archetype have been evaluated, a control strategy for applying load time shifting has been implemented. That strategy accounts for both the power demand profile and the hourly electricity price. Specifically, it has been assumed that end users access a pricing mechanism following the hourly trend of electricity economic value, which is traded day by day in the Italian spot market, instead of the current Time of Use (TOU) system. In such a way, it is possible to flatten the dwellings cluster profile, limiting undesired and unexpected results on the balancing market. In the end, monthly and yearly flexibility indexes have been defined along with the strategy effectiveness parameter. From calculations, it emerges that a dwelling cluster for the Italian residential sector is characterised by a flexibility index of 10.3% and by a strategy effectiveness equal to 34%. It is noteworthy that the highest values for flexibility purpose have been registered over the heating season (winter) for the weekends.

HEMS-IoT: A Big Data and Machine Learning-Based Smart Home System for Energy Saving

Energy efficiency has aroused great interest in research worldwide, because energy consumption has increased in recent years, especially in the residential sector. The advances in energy conversion, along with new forms of communication, and information technologies have paved the way for what is now known as smart homes. The Internet of Things (IoT) is the convergence of various heterogeneous technologies from different application domains that are used to interconnect things through the Internet, thus allowing for the detection, monitoring, and remote control of multiple devices. Home automation systems (HAS) combined with IoT, big data technologies, and machine learning are alternatives that promise to contribute to greater energy efficiency. This work presents HEMS-IoT, a big data and machine learning-based smart home energy management system for home comfort, safety, and energy saving. We used the J48 machine learning algorithm and Weka API to learn user behaviors and energy consumption patterns and classify houses with respect to energy consumption. Likewise, we relied on RuleML and Apache Mahout to generate energy-saving recommendations based on user preferences to preserve smart home comfort and safety. To validate our system, we present a case study where we monitor a smart home to ensure comfort and safety and reduce energy consumption.

Solar Energy Data Analytics: PV Deployment and Land Use

EU targets for sustainable development call for strong changes in the current energy systems as well as committed protection of environmental resources. This target conflicts if a policy is not going to promote the compatible solutions to both the issues. This is the case of the additional renewable energy sources to be exploited for increasing the share in the electricity mix and in the gross final energy consumption. Solar energy is, currently, the cheapest solution in Southern European Countries, like Italy. In this paper, thanks to the availability of three open databases provided by National Institutions, the authors compared the historic trends and policy scenarios for soil consumption, electricity consumption, and renewable electricity production to check correlations. The provincial scale was chosen as resolution of the analysis. The deviations from the policy scenarios was then addressed to identify the demand for policy recommendations and pathways to promote in order to achieve the target for renewable electricity share as well as the reduction in soli consumption trend in 2030. The role of renewables integrated in the existing contexts, such as building integrated photovoltaics, is considered a key driver for solving this issue.

How Climate Change Affects the Building Energy Consumptions Due to Cooling, Heating, and Electricity Demands of Italian Residential Sector

Climate change affects the buildings’ performance, significantly influencing energy consumption, as well as the indoor thermal comfort. As a consequence, the growing outdoor environmental temperatures entail a slight reduction in heating consumption and an increase in cooling consumption, with different overall effects depending on the latitudes. This document focuses attention on the Italian residential sector, considering the current and reduced meteorological data, in anticipation of future climate scenarios. According to a sample of 419 buildings, referring to the climatic conditions of Milan, Florence, Rome, and Naples, the heating and cooling needs are calculated by a simplified dynamic model, in current and future conditions. The effects of the simplest climate adaptation measure, represented by the introduction of new air conditioners, have been also evaluated. The simulations results show an important reduction in complex energy consumption (Milan −6%, Florence −22%, Rome −25%, Naples −30%), due to the greater incidence of heating demand in the Italian context. However, the increase in air conditioning electrical consumption over the hot season (Milan +11%, Florence +20%, Rome +19%, Naples +16%) can play a critical role for the electrical system; for that reason, the introduction of photovoltaic arrays as a compensatory measure have been analysed.

Indoor Environmental Quality Analysis for Optimizing Energy Consumptions Varying Air Ventilation Rates

The energy refurbishment of the existing building heritage is one of the pillars of Italian energy policy. Aiming for energy efficiency and energy saving in end uses, there are wide and diversified improvement strategies, which include interventions on the building envelope and Heating, Ventilation, and Air Conditioning (HVAC) systems, with the introduction of renewable energy sources. The research aims at evaluating the building energy consumptions and Indoor Environmental Quality (IEQ), varying the airflow rates handled by the HVAC system. A Case Study (the Aula Magna of a university building) is analysed; an in-situ monitoring campaign was carried out to evaluate the trend of some environmental parameters that are considered to be significant when varying the external airflow rates handled by the HVAC system. Additionally, dynamic simulations were carried out, with the aim of evaluating the energy savings coming from the airflow rates reduction. The results of this case study highlight the opportunity to achieve significant energy savings, with only slight variations in IEQ; a 50% reduction in airflow rate would decrease energy consumption by up to 45.2%, while increasing the carbon dioxide concentration from 545 ppm to 655 ppm, while the Particulate Matter and Total Volatile Organic Compounds increase is insignificant.