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

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

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.