Advancing and demonstrating the Impact Indices method to screen the sensitivity of building energy use to occupant behaviour

A critical gap between the occupant behaviour research field and the building engineering practice limits the integration of occupant-centric strategies into simulation-aided building design and operation. Closing this gap would contribute to the implementation of strategies that improve the occupants’ well-being while reducing the buildings’ environmental footprint. In this view, it is urgent to develop guidelines, standardised methods, and supporting tools that facilitate the integration of advanced occupant behaviour models into the simulation studies. One important step that needs to be fully integrated into the simulation workflow is the identification of influential and non-influential occupant behaviour aspects for a given simulation problem. Accordingly, this article advances and demonstrates the application of the Impact Indices method, a fast and efficient method for screening the potential impact of occupant behaviour on the heating and cooling demand. Specifically, the method now allows the calculation of Impact Indices quantifying the sensitivity of building energy use to occupancy, lighting use, plug-load appliances use, and blind operation at any spatial and temporal resolution. Hence, users can apply it in more detailed heating and cooling scenarios without losing information. Furthermore, they can identify which components in building design and operation require more sophisticated occupant behaviour models. An office building is used as a real case study to illustrate the application of the method and asses its performance against a one-factor-at-a-time sensitivity analysis. The Impact Indices method indicates that occupancy, lighting use and plug-load appliances have the greatest impact on the annual cooling demand of the studied office building; blind operation is influential only in the west and south façades of the building. Finally, potential applications of the method in building design and operation practice are discussed

Smart energy systems applied at urban level: the case of the municipality of Bressanone-Brixen

The present paper focuses on the energy system of the municipality of Bressanone-Brixen, located in the North of Italy. The aim of this paper is to investigate various possible energy scenarios for this case study in order to improve the overall efficiency of the system. The different scenarios include high penetration of photovoltaics at urban level, considering the maximum rooftop PV potential of the local area. Different solutions have been analyzed in order to study the handling of the consequent excess of electricity production. Electric storage and a solution combining heat pumps and thermal storage have been evaluated to maximize the local use of the generated electricity. A deterministic approach (without the use of an optimization algorithm) and a heuristic optimization approach have been applied to evaluate the different possible configurations. The present analysis can be of interest for other cities in a mountain environment where the production from renewables is limited by orographic constraints, energy consumption per capita is higher and stronger resiliency to climate change is needed

Multi-objective optimization algorithm coupled to EnergyPLAN software: The EPLANopt model

The planning of energy systems with high penetration of renewables is becoming more and more important due to environmental and security issues. On the other hand, high shares of renewables require proper grid integration strategies. In order to overcome these obstacles, the diversification of renewable energy technologies, programmable or not, coupled with different types of storage, daily and seasonal, is recommended. The optimization of the different energy sources is a multi-objective optimization problem because it concerns economical, technical and environmental aspects. The aim of this study is to present the model EPLANopt, developed by Eurac Research, which couples the deterministic simulation model EnergyPLAN developed by Aalborg University with a Multi-Objective Evolutionary Algorithm built on the Python library DEAP. The test case is the energy system of South Tyrol, for which results obtained through this methodology are presented. Particular attention is devoted to the analysis of energy efficiency in buildings. A curve representing the marginal costs of the different energy efficiency strategies versus the annual energy saving is applied to the model through an external Python script. This curve describes the energy efficiency costs for different types of buildings depending on construction period and location

Sensitivity analysis as support for reliable life cycle cost evaluation applied to eleven nearly zero-energy buildings in Europe

Life cycle cost analysis represents a strategic tool for supporting the decision-making process while designing a new building or a renovation towards a nearly zero-energy target. Nevertheless, one of the main obstacles undermining the wide application of life cycle cost analysis deals with the effort in collecting the whole set of inputs and boundary conditions and the associated reliability of the results. To address the issue, this work compares the application of different sensitivity analysis methodologies on eleven nearly zero-energy buildings with different uses and in several European contexts, highlighting the strengths and weaknesses. Moreover, it introduces and assesses an approach for applying sensitivity analysis in life cycle cost evaluations to find an effective balance between the effort for calculation, data collection and the reliability of life cycle cost. A main result is the demonstration of a sensitivity analysis procedure to identify and evaluate parameters and boundary conditions with the largest impact on the life cycle cost of the analysed buildings, namely, the interest rate, construction and equipment maintenance costs, structural element costs, and electricity prices. These parameters lead to variations in LCC of up to 37%, with an average of 26% around the median. By focusing a more detailed analysis on these parameters, we could assess the potential life cycle cost range due to input uncertainties with a high degree of confidence while keeping efforts for practitioners reasonable

Calibrating historic building energy models to hourly indoor air and surface temperatures: Methodology and case study

Abstract Uncalibrated building energy models, as well as models calibrated only on a single performance indicator such as energy consumption or indoor temperature, can be significantly unreliable regarding model parameters and other performance indicators. The risk of obtaining a calibrated model whose parameters are far from the actual values is particularly high in historic buildings because of the increased uncertainty about the building construction. In this paper, we propose a calibration methodology aimed at reducing this risk and apply it on a medieval building. The building was modeled in EnergyPlus based on an energy audit. A sensitivity analysis was performed to identify significant parameters affecting the errors between simulated and monitored indoor air temperatures. The model was calibrated on the hourly indoor air temperatures in summer by minimizing the root mean square error averaged over the building using a particle swarm optimization algorithm. A second calibration was performed by varying the parameters of a representative room. By comparing the results from these two calibrations, we obtained indications about the accuracy of the model parameters. Finally, the model was validated on hourly indoor air and surface temperatures in winter where temperature root mean square errors ranged from 0.4 to 0.8 K

Building performance evaluation through a novel feature selection algorithm for automated arx model identification procedures

ARX models are an effective instrument to evaluate continuous building performance from insufficient monitoring data. However, selecting the right model features is NP-hard. The problem of finding a minimal subset of informative inputs has been studied extensively in various fields but automatic, fast, and reliable procedures for finding optimal models for building performance evaluation are still missing. We propose a novel feature selection algorithm named Greedy Correlation Screening (GCS), which identifies a possible solution at a time by greedily maximizing the correlation between inputs and output and minimizing cross-correlations between inputs. These two objectives are competing, thus leading to best tradeoffs. Among these, the best model is automatically selected by applying filters and quality criteria such as the adjusted coefficient of correlation and non-correlation of residuals. The performance of the proposed heuristic method is compared to two of the best algorithms used in the field, such as GRASP for feature selection and NSGA-II (Non-dominated Sorting Genetic Algorithm). The application on a real case study demonstrates that the proposed method solves the problem of feature selection in building performance estimation efficiently and reliably. Moreover, the model creation is automatic, making it ideal for integration into a Building Management System (BMS) in order to detect faults and perform short-term predictive control

QUANTITATIVE COMPARISON OF MASSIVE WALLS THERMAL RESPONSE AMONG COMMERCIAL SOFTWARE

Simulating heat conduction in massive walls with commercial software is reported to cause numerical instability or reduced accur acy. As contribution to the discussion, we have simulated one-dimensional heat conduction in massive walls and their dynamic thermal responses to a step, a sinusoid and time se- ries in TRNSYS, EnergyPlus, Delphin and Matlab. As reference, we have used EN ISO 13786:2007 and a self-written Matlab response factor method imple- mentation. We have compared transient and steady- state wall surface temperatur es and heat fluxes for two different accuracy settin gs using suitable metrics. Errors up to 1 kWh/(m 2 month) have been observed

Evaluation of EN15193-1 on energy requirements for artificial lighting against Radiance-based DAYSIM

This study evaluates the calculation approach of the energy requirements for artificial lighting inside buildings of different use according to EN15193-1:2017, defining the main scope of the standard, highlighting its limitations, and proposing improvements. The evaluation was carried out through a parametric analysis to determine the influence of window-to-wall ratio, distribution of windows, presence of side opening, glazing visible trans-mittance, and overhang length on the calculation of the Lighting Energy Numeric Indicator (LENI) for a living room and an office at four representative locations (Bratislava, Stockholm, London, Athens). The standard was tested against DAYSIM, a Radiance-based simulation tool for calculating daylight availability, whose results were post-processed to obtain the energy requirements for artificial lighting. For many windows close to each other, the standard’s approach to superimpose the daylight factors (DF) for overlapping daylit areas led to an over-estimated total DF and therefore an underestimated LENI. For rooms with low window-to-facade and window-to- wall ratio, the standard’s calculation was inaccurate. The daylight supply factor tabulated in the standard was too low for latitudes below 45◦, leading to an overestimation of the LENI. For latitudes above 60◦, the opposite effect was observed. Summarising, the standard underestimated the LENI by about 10% on average

Economic and environmental impact of photovoltaic and wind energy high penetration towards the achievement of the Italian 20-20-20 targets

The paper presents an analysis of the operating parameters of the Combined Cycle Gas Turbine (CCGT) systems in Italy in the years from 2006 to 2013, studying the environmental and economic impact of renewable energy sources spread on CCGT. Variable Renewable Energy (VRE) sources development, electricity demand reduction and CCGT overcapacity have affected the CCGT systems that have had to operate at partial load, experiencing several rump-up/down cycles in a day. The consequent increase of CO2 emission and costs have to be considered in a RES high penetration future scenario. The paper aims to evaluate the net avoided emissions by RES and the cost that Italy have incurred to avoid to emit a tonne of CO2. To reach the high penetration of RES targets the attention is switching from the problem of adding more renewable energy installations to the problem of managing the whole "green" electricity production in a smarter way. Considering all the indirect effects caused by RES on the electricity system is essential for everyone who wants to analyse future scenarios