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

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

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.This work emanated from research conducted with the fi-nancial support of the European Commission through the H2020project BIGG , grant agreement 957047, and the JRC Expert Con-tractCT-EX2017D306558-102.D.ChemisanathanksICREAfortheICREA Acadèmia. Dr J. Cipriano also thanks the Ministerio deCiencia e Innovación of the Spanish Government for the Juan dela Cierva Incorporación gran

User behaviour models to forecast electricity consumption of residential customers based on smart metering data

This paper presents a novel approach to forecast day-ahead electricity consumption for residential households where highly irregular human behaviour plays a significant role. The methodology requires data from fiscal smart meters, which makes it applicable to real scenarios where personal data gathering is not feasible. These data are rarely complete; therefore, a robust combination of machine-learning techniques is used to handle missing data and outliers. The novelty of this method relies on identifying and predicting user electricity consumption behaviour as a procedure to improve the forecasting of the overall electricity consumption of each individual customer. The methodology uses Gaussian mixture clustering to identify behaviour clusters and an eXtreme Gradient Boosting classification (XGBoost) model to predict the day-ahead behaviour pattern. This predicted user behaviour cluster is fed into an Artificial Neural Network (ANN) to enable an improved capturing of the highly unpredictable user conduct for the forecast of electricity consumption. A novel metric, namely the Euclidean Distance-based Accuracy (EDA), is finally proposed to enable a more thorough evaluation of time series classification algorithms. The whole development is tested over 500 residential users placed in a southeastern region of Spain. The results showed that, when the novel approach was used, the MAPEd and NRMSEd were reduced by 7% and 9% respectively, increasing to a 20% and 17% respective reduction for the best cases according to EDA. This methodology sets the basis for massive user-centred analyses, very profitable to any electricity company.This work was developed during the PhD thesis of F. Lazzari. D. Chemisana thanks ICREA for the ICREA Acadèmia. Dr. J. Cipriano thanks the Ministerio de Ciencia e Innnovación, Spain for the Juan de la Cierva Incorporación grant. This work was also supported by the Project PID2020-113614RB-C22, funded by MCIN/AEI/10.13039/501100011033, Spain, and by the European regional Fund, through the POCTEFA program and the project EKATE-EFA 312/19. All authors approved the version of the manuscript to be publishe

Statistical learning methods for energy assessment in buildings with applications at different geographic levels

The building sector, excluding its industry, is one of the world's largest energy consumers. 2019 accounted for around 30% of the total final energy consumed worldwide. In addition, its carbon dioxide emissions accounted for 28% of the total, as much of the fuel used to generate this final energy is still of non-renewable origin. Currently, there is an extreme need to reduce these pollutant emissions over the next few years due to the global warming problems we are experiencing. In addition, the peak of fossil fuel production is either near or has already been exceeded during the last decade. This will lead to the end of affordable fossil fuels. Therefore, the world must move towards an energy strategy aimed at increasing demand-side efficiency and consuming energy produced from renewable fuels. To this end, implementing mathematical models to help characterise, simulate and predict energy consumption in the building sector is a key step in this energy transition process. Within the framework of this Thesis, a platform for storing and massively analysing energy data has been implemented. Additionally, three more specific use cases have been proposed that refer to some of the most recurrent problems at each of the main geographical levels in the building sector (dwelling, building or district level). The objectives of these use cases are to inform and alert end-users about their energy consumption, optimising energy demand or cost, maximising energy consumption from renewable generation, or inferring apparently unknown energy characteristics of buildings and their occupants. This Thesis presents the data analytics platform designed and developed to deal with the massive analysis of a vast amount of data coming from electricity smart meters. Furthermore, the implemented energy information services for end-users are presented, and the estimated energy savings generated by those services, quantified within the IEE Empowering project, are presented (3 to 22%). Subsequently, three applications are introduced, each one dealing with a specific geographical level. In the first one, a novel methodology to virtually replicate the control of thermostatically-controlled systems is presented. It is applied over a set of residential dwellings and it is based on data-driven models. Some promising outcomes showed during warm conditions (7-15ºC), for example, reducing the usual set-point temperature of the thermostat by 1ºC or 2ºC would lead to energy savings of 18.1% and 36.5% on average, respectively. In the second application, three Model Predictive Control (MPC) strategies have been implemented in different locations in Europe to assess the energy flexibility that can be achieved when a smarter control is applied to existing electricity driven heating or cooling systems in several building typologies and electricity markets. The results showed that electric heat pumps can provide significant demand response flexibility in the respective analysed electricity markets. However, they sometimes have problems regarding response time and reliability, which can affect their availability for the standby electricity market. Finally, in the third and last case study, a methodology for characterising the electricity consumption of large sets of buildings, e.g. entire districts or postal codes, is presented. The methodology is based on statistical analysis of the aggregated hourly energy consumption of the whole area of interest, as well as its correlation against meteorological information, cadastral data and socio-economic characteristics. This methodology has been validated to interpret the main drivers of electricity consumption along the whole province of Lleida (Spain).El sector de l'edificació, sense incloure la seva indústria, és un dels principals focus de consum energètic del món. Suposa al voltant d'un 30% del total d'energia final consumida mundialment. A més, les seves emissions de diòxid de carboni suposen un 28% respecte al total, ja que encara bona part del combustible utilitzat per a generar aquesta energia final és d'origen no renovable. Actualment, existeix l'extrema necessitat de reduir aquestes emissions contaminants durant els propers anys a causa dels problemes d'escalfament global que estem vivint. A més, el pic de producció dels combustibles fòssils, o és pròxim o ja l'hem sobrepassat durant l'última dècada. Aquest fet comportarà la fi dels combustibles fòssils a preu assequible. Per tant, mundialment ens hem de dirigir cap a una estratègia energètica encaminada a incrementar l'eficiència en la demanda i a consumir energia produïda mitjançant combustibles renovables. A aquest efecte, la implementació de models matemàtics que ajudin a caracteritzar, simular i a predir el consum energètic en el sector de l'edificació suposa un pas clau en aquest procés de transició energètica. En el marc d'aquesta Tesi s'ha implementat una plataforma per emmagatzemar i analitzar massivament dades energètiques, i s'han plantejat tres casos d'ús més concrets que fan referència a algunes de les problemàtiques més recurrents en cadascun dels principals nivells geogràfics en el sector edificació (nivell habitatge, edifici, o districte). Els objectius d'aquestes analítiques són informar i alertar a usuaris finals sobre el seu consum, optimitzar la demanda o el cost energètic, maximitzar el consum procedent de producció energètica renovable, o inferir característiques energètiques. Primerament, aquesta Tesi presenta la plataforma d'analítica dissenyada per a l'anàlisi massiva de comptadors intel·ligents d'electricitat. A part, es detallen els serveis d'informació energètica per a usuaris finals que s'han implementat, i es presenten els resultats d'estalvi estimat produït (del 3 al 22%) al llarg d'un projecte amb tres comercialitzadores d'electricitat europees. Posteriorment, es presenten les tres aplicacions específiques tractant diferents nivells geogràfics. En la primera d'elles, es presenta una novedosa metodologia per tal de replicar virtualment el control dels sistemes comandats per termòstat en el sector residencial utilitzant models basats en dades. Els resultats d'aquesta recerca mostren que es pot aconseguir un estalvi energètic del 18,1% i del 36,5% de mitjana, si es redueix la temperatura de consigna habitual en 1ºC i 2ºC, respectivament. En la segona aplicació, tres estratègies de Control Predictiu mitjançant Models (MPC, en anglès) s'han implementat en tres llocs diferents d'Europa, amb l'objectiu d'avaluar la flexibilitat energètica que pot aconseguir-se quan s'aplica un control més intel·ligent a sistemes de calefacció existents d'un edifici o d'un conjunt molt petit d'edificis. Els resultats del mètode mostren que les bombes de calor tenen el potencial de proporcionar una important flexibilitat de resposta a la demanda als països analitzats. No obstant això, a vegades tenen problemes quant al seu temps de resposta i fiabilitat, la qual cosa pot afectar la seva disponibilitat per al mercat de reserva d'electricitat. En la tercera i última aplicació, es presenta una metodologia de caracterització del consum elèctric de grans conjunts d'edificis, per exemple districtes sencers o codis postals. Es basa en l'anàlisi estadística dels consums energètics horaris agregats a les diferents arees d'interès, i la seva correlació respecte informació meteorològica, cadastral o característiques socioeconòmiques. Aquest mètode s'ha validat per a interpretar els factors de canvi en el consum elèctric de la província de Lleida (Espanya).El sector de la edificación, sin incluir la industria, es uno de los principales focos de consumo energético del mundo. Supone alrededor de un 30% del total de energía final consumida mundialmente. Además, sus emisiones de dióxido de carbono suponen un 28% respecto al total, ya que todavía buena parte del combustible utilizado para generar esta energía final es de origen no renovable. Actualmente, existe la extrema necesidad de reducir estas emisiones contaminantes durante los siguientes años debido a los problemas de calentamiento global que estamos viviendo. Además, el pico de producción de los combustibles fósiles, o es cercano o ya lo hemos sobrepasado durante la última década. Este hecho conllevará el fin de los combustibles fósiles a precio asequible. Por lo tanto, el mundo debe dirigirse hacia una estrategia energética encaminada a incrementar la eficiencia en la demanda y a consumir energía producida mediante combustibles renovables. Con este fin, la implementación de modelos matemáticos que ayuden a caracterizar, simular y a predecir el consumo energético en el sector de la edificación supone un paso clave en este proceso de transición energética. En el marco de esta Tesis se ha implementado una plataforma para almacenar y analizar masivamente datos energéticos, y se han planteado tres casos de uso más concretos que hacen referencia a algunas de las problemáticas más recurrentes en cada uno de los principales niveles geográficos en el sector edificación (nivel vivienda, edificio, o distrito). Los objetivos de estas analíticas son informar y alertar a usuarios finales sobre su consumo energético, optimizar la demanda o el coste energético, maximizar el consumo procedente de producción renovable, o inferir características energéticas aparentemente desconocidas. Inicialmente, esta Tesis presenta la plataforma de analítica diseñada para el análisis masivo de contadores inteligentes de electricidad. Aparte, se detallan los servicios de información energética para usuarios finales implementados, y se presentan los resultados de ahorro estimado producido (3% a 22%) a lo largo del proyecto IEE Empowering para tres comercializadoras de electricidad. Posteriormente, se presentan tres aplicaciones específicas tratando distintos niveles de agregación. En la primera de ellas, se presenta una metodología novedosa para replicar virtualmente el control de los sistemas comandados por termostato en el sector residencial utilizando modelos basados en datos. Los resultados de esta investigación muestran que se puede conseguir un ahorro energético del 18,1% y del 36,5% de media, si se reduce la temperatura de consigna habitual en 1ºC y 2ºC, respectivamente. En la segunda aplicación se han implementado tres estrategias de Control Predictivo mediante Modelos (MPC, en inglés) en tres lugares distintos de Europa, con el objetivo de evaluar la flexibilidad energética que puede lograrse cuando se aplica un control más inteligente a sistemas de calefacción eléctricos existentes en un edificio o un conjunto muy pequeño de edificios. Los resultados del método muestran que las bombas de calor tienen el potencial de proporcionar una importante flexibilidad de respuesta a la demanda en los países analizados. Sin embargo, en ocasiones tienen problemas en cuanto a su tiempo de respuesta y fiabilidad, lo que puede afectar a su disponibilidad para el mercado de reserva de electricidad. En la tercera y última aplicación, se presenta una metodología de caracterización del consumo eléctrico sobre grandes conjuntos de edificios, por ejemplo distritos enteros o códigos postales. Se basa en el análisis estadístico de los consumos energéticos horarios agregados a cada una de las áreas de interés, y su correlación con la información meteorológica, catastral y las características socioeconómicas. Este método se ha validado para interpretar los factores de cambio en el consumo eléctrico de la provincia de Lleida (España)

Beyond the imitation game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting.Comment: 27 pages, 17 figures + references and appendices, repo: https://github.com/google/BIG-benc