Empowering customer engagement by informative billing: a European approach

Programmes aimed at improving end-use energy efficiency are a keystone in the market strategies of leading distribution system operators (DSOs) and energy retail companies and are increasing in application, soon expected to become a mainstream practice. Informative services based on electricity meter data collected for billing are powerful tools for energy savings in scale and increase customer engagement with the energy suppliers enabling the deployment of demand response programmes helping to optimise distribution grid operation. These services are completely in line with Europe’s 2020 strategy for overall energy performance improvement (cf. directives 2006/32/EC, 2009/72/EC, 2012/27/EU). The Intelligent Energy Europe project EMPOWERING involves 4 European utilities and an international team of university researchers, social scientists and energy experts for developing and providing insight based services and tools for 344.000 residential customers in Austria, France, Italy and Spain. The project adopts a systematic iterative approach of service development based on envisaging the utilities’, customers’ and legal requirements, and incorporates the feedback from testing in the design process. The technological solution provided by the leading partner CIMNE is scalable open source Big Data Analytics System coupled with the DSO’s information systems and delivering a range of value adding services for the customer, such as: - comparison with similar households - indications of performance improvements over time - consumption-weather dependence - detailed consumption visualisation and breakdown - personalised energy saving tips - alerts (high consumption, high bill, extreme temperature, etc.) The paper presents the development approach, describes the ICT system architecture and analyses the legal and regulatory context for providing this kind of services in the European Community. The limitations for third party data access, customer consent and data privacy are discussed, and how these have been overcome with the implementation of the “privacy by design” principle is explained

Development of experimental and numerical infrastructures for the study of compact heat exchangers and liquid overfeed refrigeration systems

Se ha desarrollado y construido una infraestructura experimental orientada a la validación de modelos de intercambiadores compactos de aletas y tubos y sistemas de refrigeración con sobrealimentación de líquido. El objetivo ha sido la obtención de datos experimentales fiables, con condiciones geométricas y de contorno exactamente definidas, para poder compararlos inequívocamente con resultados de simulaciones numéricas. Se presentan los modelos matemáticos, objetivo de la validación, y una descripción detallada del circuito de aire, del refrigerante líquido, y del refrigerante de cambio de fase, que integran la infraestructura.Estos tres circuitos están encargados de asegurar condiciones estables y controladas para los prototipos ensayados y para el sistema de refrigeración con sobrealimentación de líquido, en un amplio rango de temperaturas, flujos másicos y potencias. El diseño permite el ensayo de prototipos de intercambiadores de calor con diferentes geometrías y dimensiones. Se presentan detalladamente los instrumentos de medida con sus precisiones, montaje, se describen también los componentes y los parámetros de la unidad de adquisición de datos.Especial atención se ha dedicado a la calibración de los instrumentos de medida como parte esencial del proceso de preparación de los ensayos. Se describe el proceso de estimación de las incertidumbres sistemáticas de los sensores calibrados. Se expone en detalle la formulación y la metodología adoptada para el análisis de incertidumbre de los resultados experimentales.El procesamiento y el análisis de los datos experimentales se ha realizado en forma automática con un código computacional especialmente desarrollado, encargado de calcular los resultados a partir de las variablas medidas, de llevar a cabo el análisis de incertidumbres detallado, y de comparar los resultados numéricos y experimentales.Se presentan resultados experimentales obtenidos con la infraestructura experimental desarrollada. Se presentan estudios detallados de intercambiadores de calor compactos en condiciones de enfriamiento de aire, utilizando refrigerante líquido y de cambio de fase. Se presentan también resultados del estudio experimental del sistema de refrigeración con sobrealimentación de líquido. Los resultados han sido comprobados y verificados a través de balances energéticos en todos los componentes, donde la misma magnitud física ha sido evaluada de mediciones independientes. Con el objetivo de permitir el uso mas general de los resultados experimentales se presentan también los datos crudos de las variables medidas durante los ensayos.Se ha propuesto una metodología de validación para el modelo de intercambiadores compactos, basada en comparaciones sistemáticas de resultados numéricos y experimentales. Estas comparaciones han sido analizadas en términos estadísticos con el objetivo de cuantificar las diferencias observadas y dar una evaluación global de las prestaciones del modelo numérico en las condiciones ensayadas. La metodología propuesta para la validación del modelo de intercambiadores compactos puede ser utilizada como base para metodologías de validación en general.Experimental infrastructures intended for validation of compact heat exchanger models, and models of liquid overfeed refrigeration systems have been developed and constructed. The aim has been the obtaining of reliable experimental data from tests at exactly defined geometrical and boundary conditions, permitting the unequivocal comparisons with numerical simulation results. The mathematical models are presented and detailed description of the airhandling, the liquid refrigerant, and phase-changing refrigerant circuits integrating the experimental infrastructure is given.These three circuits are encharged to provide stable controlled conditions for the tested prototypes and the liquid overfeed system in the desired range of temperatures, fluid flows, and capacities. The design permits the accommodation of heat exchanger prototypes with different geometry and sizes.Detailed overview of the measuring instruments is presented, with their accuracies and mounting, and the components and parameters of the data acquisition system are described.Special attention has been paid to the calibration of the measuring instruments as an essential part of the test preparation. The process of estimation of the systematic uncertainties in the calibrated sensors measurements is described. The formulation and the methodology adopted for the uncertainty analysis of the experimental results is exposed in detail.The experimental data processing and analysis has been performed automatically with a specially developed program encharged with the calculation of the experimental results from the measured variables, the detailed uncertainty analysis, and the numerical to experimental results comparisons.Experimental results obtained with the developed infrastructure are presented. Detailed studies of compact heat exchangers under cooling conditions, using liquid and phase-changing refrigerants, are performed and presented. Results from the experimental studies of the liquid overfeed refrigeration system are also presented. The results have been checked and verified through energy balance checks for all the components where measurements of the same physical magnitude can be contrasted with independent measurements. In order to give more general use of the obtained experimental data, the raw measured variables during the tests are also presented.An experimental validation methodology for the compact heat exchanger model has been proposed, based on systematic comparisons between numerical and experimental results. The comparisons have been analysed in statistical terms in order to quantify the observed differences and to give global evaluation of the numerical model performance in the tested conditions. The methodology proposed for validation of the heat exchanger model can be used as a basis for validation methodology for numerical models in general

Influencing factors in energy use of housing blocks: a new methodology, based on clustering and energy simulations, for decision making in energy refurbishment projects

In recent years, big efforts have been dedicated to identify which are the factors with highest influence in the energy consumption of residential buildings. These factors include aspects such as weather dependence, user behaviour, socio-economic situation, type of the energy installations and typology of buildings. The high number of factors increases the complexity of analysis and leads to a lack of confidence in the results of the energy simulation analysis. This fact grows when we move one step up and perform global analysis of blocks of buildings. The aim of this study is to report a new methodology for the assessment of the energy performance of large groups of buildings when considering the real use of energy. We combine two clustering methods, Generative Topographic Mapping and k-means, to obtain reference dwellings that can be considered as representative of the different energy patterns and energy systems of the neighbourhood. Then, simulation of energy demand and indoor temperature against the monitored comfort conditions in a short period is performed to obtain end use load disaggregation. This methodology was applied in a district at Terrassa City (Spain), and six reference dwellings were selected. Results showed that the method was able to identify the main patterns and provide occupants with feasible recommendations so that they can make required decisions at neighbourhood level. Moreover, given that the proposed method is based on the comparison with similar buildings, it could motivate building occupants to implement community improvement actions, as well as to modify their behaviour

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

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.This research has received funding from the European Union’s Horizon 2020 research and innovation programme under the ENTRACK project [Grant Agreement 885395

EMPOWERING, a smart Big Data framework for sustainable electricity suppliers

This paper presents the EMPOWERING project, a Big Data environment aimed at helping domestic customers to save electricity by managing their consumption positively. This is achieved by improving the information received about energy bills and offering online tools. The main contributions of EMPOWERING are the creation of a novel workflow in the electricity utility sector regarding the implementation of data analytics for their customers and the fast implementation of data-mining techniques in massive datasets within a Big Data platform to achieve scalability. The results obtained show that EMPOWERING can be of use for customers of electrical suppliers by changing their energy habits to decrease consumption and so increase environmental sustainability

Using matrix factorisation for the prediction of electrical quantities

The prediction task is attracting more and more attention among the power system community. Accurate predictions of electrical quantities up to a few hours ahead (e.g. renewable production, electrical load etc.) are for instance crucial for distribution system operators to operate their network in the presence of a high share of renewables, or for energy producers to maximise their profits by optimising their portfolio management. In the literature, statistical approaches are usually proposed to predict electrical quantities. In the present study, the authors present a novel method based on matrix factorisation. The authors' approach is inspired by the literature on data mining and knowledge discovery and the methodologies involved in recommender systems. The idea is to transpose the problem of predicting ratings in a recommender system to a problem of forecasting electrical quantities in a power system. Preliminary results on a real wind speed dataset tend to show that the matrix factorisation model provides similar results than auto regressive integrated models in terms of accuracy (MAE and RMSE). The authors' approach is nevertheless highly scalable and can deal with noisy data (e.g. missing data)

Influencing factors in energy use of housing blocks: a new methodology, based on clustering and energy simulations, for decision making in energy refurbishment projects

In recent years, big efforts have been dedicated to identify which are the factors with highest influence in the energy consumption of residential buildings. These factors include aspects such as weather dependence, user behaviour, socio-economic situation, type of the energy installations and typology of buildings. The high number of factors increases the complexity of analysis and leads to a lack of confidence in the results of the energy simulation analysis. This fact grows when we move one step up and perform global analysis of blocks of buildings. The aim of this study is to report a new methodology for the assessment of the energy performance of large groups of buildings when considering the real use of energy. We combine two clustering methods, Generative Topographic Mapping and k-means, to obtain reference dwellings that can be considered as representative of the different energy patterns and energy systems of the neighbourhood. Then, simulation of energy demand and indoor temperature against the monitored comfort conditions in a short period is performed to obtain end use load disaggregation. This methodology was applied in a district at Terrassa City (Spain), and six reference dwellings were selected. Results showed that the method was able to identify the main patterns and provide occupants with feasible recommendations so that they can make required decisions at neighbourhood level. Moreover, given that the proposed method is based on the comparison with similar buildings, it could motivate building occupants to implement community improvement actions, as well as to modify their behaviour.Peer ReviewedPostprint (author's final draft

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

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.This work was supported by the European Commission through the H2020 project SENSEI [grant number 847066]. The authors thank the Catalan Institute of Energy (ICAEN) for providing the monitoring and EEM data that was analysed in the case study

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

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.Peer ReviewedPostprint (author's final draft

Approaches to evaluate building energy performance from daily consumption data considering dynamic and solar gain effects

A method for determining the total heat loss coefficient, the effective heat capacity and the net solar gain of a building is presented. The method uses a linear regressions approach based on daily energy consumption combined with readily available meteorological data. The effective heat capacity of the building is evaluated by correlating the energy consumption and outdoor temperature changes from the previous day. The net solar gain of the building is assessed by analysing the data separated into groups by amount of daily solar irradiation. Corrected total heat loss coefficient is determined by explicitly including in the building's energy balance the accumulated heat and the solar gain. The method has been applied to the analysis of nine public buildings in Spain. An improvement of the estimated heat loss coefficient due to the corrections is observed. The effective heat capacity normalised by the building area is shown to be a useful indicator of the building operation, detecting continuous or intermittent heating. The estimated parameters in this study can enable specific benchmarking, detecting opportunities for energy savings and evaluating their potential. With the increasing implementation of smart metering technologies, the method is promising for application to the analysis of large building stocks. &nbsp