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Documents published in Scipedia

• Data-Driven Virtual Replication of Thermostatically Controlled Domestic Heating Systems

  G. Mor Martinez, J. Cipriano, E. Gabaldon, B. Grillone, D. Chemisana

  Energies (2021). -

  
  

  Abstract

  Thermostatic load control systems are widespread in many countries. Since they provide heat for domestic hot water and space heating on a massive scale in the residential sector, the assessment of their energy performance and the effect of different control strategies requires simplified modeling techniques demanding a small number of inputs and low computational resources. Data-driven techniques are envisaged as one of the best options to meet these constraints. This paper presents a novel methodology consisting of the combination of an optimization algorithm, two auto-regressive models and a control loop algorithm able to virtually replicate the control of thermostatically driven systems. This combined strategy includes all the thermostatically controlled modes governed by the set point temperature and enables automatic assessment of the energy consumption impact of multiple scenarios. The required inputs are limited to available historical readings from smart thermostats and external climate data sources. The methodology has been trained and validated with data sets coming from a selection of 11 smart thermostats, connected to gas boilers, placed in several households located in north-eastern Spain. Important conclusions of the research are that these techniques can estimate the temperature decay of households when the space heating is off as well as the energy consumption needed to reach the comfort conditions. The results of the research also show that estimated median energy savings of 18.1% and 36.5% can be achieved if the usual set point temperature schedule is lowered by 1○C and 2○C, respectively.

  Abstract
  Thermostatic load control systems are widespread in many countries. Since they provide heat for domestic hot water and space heating on a massive scale in the residential [...]

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• A data-driven method for unsupervised electricity consumption characterisation at the district level and beyond

  G. Mor Martinez, J. Cipriano, G. Martirano, F. Pignatelli, C. Lodi, F. Lazzari, B. Grillone, D. Chemisana

  Energy Reports (2021). -

  
  

  Abstract

  A bottom-up electricity characterisation methodology of the building stock at the local level is presented. It is based on the statistical learning analysis of aggregated energy consumption data, weather data, cadastre, and socioeconomic information. To demonstrate the validity of this methodology, the characterisation of the electricity consumption of the whole province of Lleida, located in northeast Spain, is implemented and tested. The geographical aggregation level considered is the postal code since it is the highest data resolution available through the open data sources used in the research work. The development and the experimental tests are supported by a web application environment formed by interactive user interfaces specifically developed for this purpose. The paper’s novelty relies on the application of statistical data methods able to infer the main energy performance characteristics of a large number of urban districts without prior knowledge of their building characteristics and with the use of solely measured data coming from smart meters, cadastre databases and weather forecasting services. A data-driven technique disaggregates electricity consumption in multiple uses (space heating, cooling, holidays and baseload). In addition, multiple Key Performance Indicators (KPIs) are derived from this disaggregated energy uses to obtain the energy characterisation of the buildings within a specific area. The potential reuse of this methodology allows for a better understanding of the drivers of electricity use, with multiple applications for the public and private sector.

  Abstract
  A bottom-up electricity characterisation methodology of the building stock at the local level is presented. It is based on the statistical learning analysis of aggregated [...]

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• Data-driven method for unsupervised electricity consumption characterisation at district level and beyond

  G. Mor Martinez, G. Martirano, F. Pignatelli

  (2021). JRC

  
  

  Abstract

  Enhancing energy efficiency has become a priority for the European Union. Several policies and initiatives aim to improve the energy performance of buildings and collect data of sufficient quality on the effect of energy efficiency policies on building stocks across Europe. Knowledge about the characteristics of the building stock and the usage of these buildings' occupants is essential for defining and assessing strategies for energy savings. Nowadays, dynamic measured data from the Advanced Metering Infrastructure (AMI), especially in electricity consumption, combined with location-based data, like weather, cadastre, social or economic conditions, should be available for a significant part of the building stocks in Europe. Combinedly, this enormous set of data contains the characteristics of how buildings and their occupants consume energy. In this document, a bottom-up electricity characterisation methodology of the building stock at the local level is presented. It is based on the statistical analysis of aggregated energy consumption data, weather data, cadastre, and socioeconomic information. For validation purposes, the characterisation of the electricity consumption over Lleida (Spain) province is performed. The geographical aggregation level considered is the postal code (more detailed than LAU level 2, formerly NUTS level 5), due to it is the highest resolution available through the Spanish Distribution System Operators (DSOs) data portal. Besides, a web application to visualise the results of the characterisation has also been developed. The major novelty is the use of high-frequency consumption data from most consumers in each analysis area without considering any Building Energy Simulation (BES) model that considers performance or energy use assumptions. For this purpose, a data-driven technique is used to disaggregate consumption due to multiple components (heating, cooling, holiday and baseload). In addition, multiple Key Performance Indicators (KPIs) are derived from these components to obtain the characterisation results. The potential reuse of this methodology allows for a better understanding of the drivers of electricity use, with multiple applications for the public and private sectors. This study has been executed in the frame of the Energy & Location Applications of the ELISE (European Location Interoperability Solutions for e-Government) action of the ISA 2 (Interoperability solutions for public administrations, businesses and citizens) Programme.

  Abstract
  Enhancing energy efficiency has become a priority for the European Union. Several policies and initiatives aim to improve the energy performance of buildings and collect [...]

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• Baseline Energy Use Modeling and Characterization in Tertiary Buildings Using an Interpretable Bayesian Linear Regression Methodology

  B. Grillone, G. Mor Martinez, S. Danov, J. Cipriano, F. Lazzari, A. Sumper

  Benedetto Grillone's personal collection (2021). 2

  
  

  Abstract

  Interpretable and scalable data-driven methodologies providing high granularity baseline predictions of energy use in buildings are essential for the accurate measurement and verification of energy renovation projects and have the potential of unlocking considerable investments in energy efficiency worldwide. Bayesian methodologies have been demonstrated to hold great potential for energy baseline modelling, by providing richer and more valuable information using intuitive mathematics. This paper proposes a Bayesian linear regression methodology for hourly baseline energy consumption predictions in commercial buildings. The methodology also enables a detailed characterization of the analyzed buildings through the detection of typical electricity usage profiles and the estimation of the weather dependence. The effects of different Bayesian model specifications were tested, including the use of different prior distributions, predictor variables, posterior estimation techniques, and the implementation of multilevel regression. The approach was tested on an open dataset containing two years of electricity meter readings at an hourly frequency for 1578 non-residential buildings. The best performing model specifications were identified, among the ones tested. The results show that the methodology developed is able to provide accurate high granularity baseline predictions, while also being intuitive and explainable. The building consumption characterization provides actionable information that can be used by energy managers to improve the performance of the analyzed facilities.

  Abstract
  Interpretable and scalable data-driven methodologies providing high granularity baseline predictions of energy use in buildings are essential for the accurate measurement [...]

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• A data-driven methodology for enhanced measurement and verification of energy efficiency savings in commercial buildings

  B. Grillone, G. Mor Martinez, S. Danov, J. Cipriano, A. Sumper

  Applied Energy (2021).

  
  

  Abstract

  Methods to obtain accurate estimations of the savings generated by building energy efficiency interventions are a topic of great importance, and considered to be one of the keys to increase capital investments in energy conservation strategies worldwide. In this study, a novel data-driven methodology is proposed for the measurement and verification of energy efficiency savings, with special focus on commercial buildings and facilities. The presented approach involves building use characterization by means of a clustering technique that allows to extract typical consumption profile patterns. These are then used, in combination with an innovative technique to evaluate the building’s weather dependency, to design a model able to provide accurate dynamic estimations of the achieved energy savings. The method was tested on synthetic datasets generated using the building energy simulation software EnergyPlus, as well as on monitoring data from real-world buildings. The results obtained with the proposed methodology were compared with the ones provided by applying the time-of-week-and-temperature (TOWT) model, showing up to 10% CV(RMSE) improvement, depending on the case in analysis. Furthermore, a comparison with the deterministic results provided by EnergyPlus showed that the median estimated savings error was always lower than 3% of the total reporting period consumption, with similar accuracy retained even when reducing the total training data available.

  Abstract
  Methods to obtain accurate estimations of the savings generated by building energy efficiency interventions are a topic of great importance, and considered to be one of the [...]

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• A review of deterministic and data-driven methods to quantify energy efficiency savings and to predict retrofitting scenarios in buildings

  B. Grillone, S. Danov, A. Sumper, J. Cipriano, G. Mor Martinez

  A review of deterministic and data-driven methods to quantify energy efficiency savings and to predict retrofitting scenarios in buildings (2020).

  
  

  Abstract

  Increasing the energy efficiency of the built environment has become a priority worldwide and especially in Europe. Because of the relatively low turnover rate of the existing built environment, energy efficiency retrofitting appears to be a fundamental step in reducing its energy consumption. Last experiences have shown that there is a vast energy efficiency potential lying in the building stock, and it is mainly untapped. One of the reasons is a lack of robust methodologies able to evaluate the effect of applied energy efficiency measures and inform about the expected impact of potential retrofitting strategies. Nowadays, dynamic measured data coming from automated metering infrastructure provides valuable information to evaluate the effect of energy conservation strategies. For this reason, energy performance modeling and assessment methods based on this data are starting to play a major role. In this paper, several methodologies for the measurement and verification of energy savings, and for the prediction and recommendation of energy retrofitting strategies, are analysed in detail. Practitioners looking at different options for these two processes, will find in this review a thorough and detailed overview of the different methods that can be used. Guidance is also provided to determine which method could work best depending on the specific case under analysis. The reviewed approaches include statistical learning models, machine learning models, Bayesian methods, deterministic approaches, and hybrid techniques that combine deterministic and data-driven modeling. Existing research gaps are identified and prospects for future investigation are presented within the main conclusions of this research work.

  Abstract
  Increasing the energy efficiency of the built environment has become a priority worldwide and especially in Europe. Because of the relatively low turnover rate of the existing [...]

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• Model Predictive Control applied to thermostatic controlled systems

  F. Lazzari, G. Mor Martinez, J. Cipriano, E. Gabaldon, S. Danov, B. Grillone

  Florencia Lazzari's personal collection (2020). 1

  
  

  Abstract

  A Model Predictive Control (MPC) was developed to optimize the performance of a residential heat pump. To test the MPC, the real system was replaced by a Modelica emulator of the entire house. The building’s thermal behavior model was developed as an Auto-Regressive linear model with eXogenous variables (ARX) and the optimization algorithm was based in a genetic algorithm. The proposed predictive model to characterize the building’s thermal behavior was developed successfully. And the MPC is already implemented and validated, but the total integration with the emulator of the system and the feedback loop has not been yet assembled. In this way, we are looking towards to end this task and complete the further analysis.

  Abstract
  A Model Predictive Control (MPC) was developed to optimize the performance of a residential heat pump. To test the MPC, the real system was replaced by a Modelica emulator [...]

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• Evaluation of a multi-stage guided search approach for the calibration of building energy simulation models

  J. Cipriano, G. Mor Martinez, D. Chemisana, D. Pérez, G. Gamboa, X. Cipriano

  Energy and Buildings (2015). Vol. 87, pp. 370-385

  
  

  Abstract

  This paper is focused on increasing the knowledge on methods for calibrating BES models and to get more insights of different approaches for the optimization of the calibration process. The paper will be centred in the evaluation of a multistage guided search approach. It defines an iterative optimization procedure which starts with the assignment of probabilistic density functions to the unknown parameters, followed by a random sampling and running batch of simulations. It then finishes with an iterative uncertainty and sensitivity analysis combined with a re-assignment of the ranges of variation of the strong parameters. The procedure converges when no new influencing parameters are found. This method is applied to a real case study consisting of an unoccupied office building located in Lleida (Spain). The measured indoor temperature has been used to determine the uncertainty and precision of the method. The effect of the size of the sampling, the number of iterations and the parameters of the global sensitivity method are analyzed in detail. The results of this paper exemplify the degree of accuracy of multistage guided search approaches, and illustrate the reasons how these analyses can contribute to the improvement of more refined calibration methods.

  Abstract
  This paper is focused on increasing the knowledge on methods for calibrating BES models and to get more insights of different approaches for the optimization of the [...]

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• Development of a dynamic model for natural ventilated photovoltaic components and of a data driven approach to validate and identify the model parameters

  J. Cipriano, G. Houzeaux, G. Mor Martinez, U. Eicker, J. Carbonell, S. Danov

  Solar Energy (2016). Vol. 129, pp. 310-331

  
  

  Abstract

  In the development of dynamic models for the energy performance evaluation of building integrated natural ventilated PV components there are still many open questions regarding the uncertainty of the estimated parameters of the models. Traditionally, the dynamic models for these complex components are derived from the heat transfer balance equations, and the unknown heat transfer coefficients (convection and radiation), the solar properties of the materials or the pressure coefficients for the air mass flow rate balance, are assigned based on literature or on manufacturer prescriptions. However, there is a lack of systematic methods able to validate the simulation outputs with the measured data, taking into consideration the uncertainty of the parameters and their effect over the results. This research is focused on the development of a dynamic simulation model for a PV ventilated component, and on the application of a data-driven iterative approach to identify the unknown parameters, to evaluate their influence in the simulation outputs and finally, to determine the deviations of the simulations outputs against the measured data. During the identification process, 43 unknown parameters are detected and 13 of them are categorized as strong parameters. The implemented data driven approach is able to achieve high goodness of fit with the measured data and it is recommended to analyses which aim at evaluating the influence of some component parameters or the thermal and electrical energy produced by these natural ventilated PV components.

  Abstract
  In the development of dynamic models for the energy performance evaluation of building integrated natural ventilated PV components there are still many open questions regarding [...]

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• Hidden Markov Models for indirect classification of occupant behaviour

  J. Liisberg, J. Moller, H. Bloem, J. Cipriano, G. Mor Martinez, H. Madsen

  Sustainable Cities and Society (2016). Vol. 27, pp. 83-98

  
  

  Abstract

  Even for similar residential buildings, a huge variability in the energy consumption can be observed. This variability is mainly due to the different behaviours of the occupants and this impacts the thermal (temperature setting, window opening, etc.) as well as the electrical (appliances, TV, computer, etc.) consumption. It is very seldom to find direct observations of occupant presence and behaviour in residential buildings. However, given the increasing use of smart metering, the opportunity and potential for indirect observation and classification of occupants’ behaviour is possible. This paper focuses on the use of Hidden Markov Models (HMMs) to create methods for indirect observations and characterisation of occupant behaviour. By applying homogeneous HMMs on the electricity consumption of fourteen apartments, three states describing the data were found suitable. The most likely sequence of states was determined (global decoding). From reconstruction of the states, dependencies like ambient air temperature were investigated. Combined with an occupant survey, this was used to classify/interpret the states as (1) absent or asleep, (2) home, medium consumption and (3) home, high consumption. From the global decoding, the average probability profiles with respect to time of day were investigated, and four distinct patterns of occupant behaviour were observed. Based on the initial results of the homogeneous HMMs and with the observed dependencies, time dependent HMMs (inhomogeneous HMMs) were developed, which improved forecasting. For both the homogeneous and inhomogeneous HMMs, indications of common parameters were observed, which suggests further development of the HMMs as population models.

  Abstract
  Even for similar residential buildings, a huge variability in the energy consumption can be observed. This variability is mainly due to the different behaviours of the occupants [...]

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