Unsupervised recognition and prediction of daily patterns in heating loads in buildings

This paper presents a multistep methodology combining unsupervised and supervised learning techniques for the identification of the daily heating energy consumption patterns in buildings. The relevant number of typical profiles is obtained through unsupervised clustering processes. Then Classification and Regression Trees are used to predict the profile type corresponding to external variables, including calendar and climatic variables, from any given day. The methodology is tested with a variety of datasets for three different buildings with different uses connected to the district heating network in Tartu (Estonia). The three buildings under analysis present different energy behaviors (residential, kindergarten and commercial buildings). The paper shows that unsupervised clustering is effective for pattern recognition since the results from the classification and regression trees match the results from the unsupervised clustering. Three main patterns have been identified in each building, seasonality and daily mean temperature being the variables that have the greatest effect. The results concluded that the best classification accuracy is obtained with a small number of clusters with a classification accuracy from 0.7 to 0.85, approximately.The authors would like to thank GREN Eesti [44] for providing data from the substations for academic purposes. The authors would like to acknowledge the Spanish Ministry of Science and Innovation (MICINN) for funding through the Sweet-TES research project (RTI2018-099557-B-C22). This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 768567

Automatic fouling detection in district heating substations: Methodology and tests

Abstract Diagnosis of anomalies in heat exchangers of district heating substations is an essential point to assure high comfort level in buildings, as well as to exploit energy sources efficiently. The aim of this paper is to propose a methodology for automatically detecting fouling in the heat exchangers located in the substations of a district heating system. The methodology is tailored for large district heating networks, where a large number of buildings should be examined with reasonable availability of data. Fouling is analysed using only the data collected by the meters installed in the substations: the mass flow rate on the primary side and the temperatures on both sides of the heat exchanger. Evaluation is difficult due to the rawness of the data gathered and the variable operating conditions, which are adjusted on the basis of the external temperatures and set-points. The software created to implement the proposed methodology receives rough data as the input and it is able to manage data gap and lack of data. Furthermore, it provides a graphical output, which can be used for assisting the operators who manage the network and plan the cleaning schedules. The software has been tested considering space heating substations in six distribution networks of the Turin district heating system, for a total amount of 325 heat exchangers. A regular application of the approach and the cleaning of the heat exchangers presenting fouling is expected to lead to an average annual decrease of about 1.6% of the primary energy consumption in the entire network

An Innovative Control Framework for District Heating Systems: Conceptualisation and Preliminary Results

This paper presents a holistic innovative solution for the transformation of the current district heating and cooling systems to automated more efficient systems. A variety of technological advancements have been developed and integrated to support the effective energy management of future district heating and cooling sector. First, we identify and discuss the main challenges and needs that are in line with the EU objectives and policy expectations. We give an overview of the main parts that our solution consists of, with emphasis on the forecasting tools and an advanced control system that addresses unit commitment and economic load dispatch problems. The proposed control approach employs distributed and scalable optimisation algorithms for optimising the short-term operations of a district heating and cooling plant subject to technical constraints and uncertainties in the energy demand. To test the performance and validate the proposed control system, a district heating plant with multiple energy generation units and real-life heat load data were used. Simulation experiments were also used to evaluate the benefits of using thermal storage units in district heating systems. The results show that the proposed method could achieve significant cost savings when energy storage is employed. The proposed control strategy can be applied for both operating optimally district heating plants with storage and supporting investment planning for new storage units

Analysis on the Application of Machine-Learning Algorithms for District-Heating Networks' Characterization & Management

359 p.Esta tesis doctoral estudia la viabilidad de la aplicación de algoritmos de aprendizaje automático para la caracterización energética de los edificios en entornos de redes de calefacción urbana. En particular, la disertación se centrará en el análisis de las siguientes cuatro aplicaciones principales: (i)La identificación y eliminación de valores atípicos de demanda en los edificios; (ii) Reconocimiento de los principales patrones de demanda energética en edificios conectados a la red. (iii) Estudio de interpretabilidad/clasificación de dichos patrones energéticos. Análisis descriptivo de los patrones de la demanda. (iv) Predicción de la demanda de energía en resolución diaria y horaria.El interés de la tesis fue despertado por la situación energética actual en la Unión Europea, donde los edificios son responsables de más del 40% del consumo total de energía. Las redes de distrito modernas han sido identificadas como sistemas eficientes para el suministro de energía desde las plantas de producción hasta los consumidores finales/edificios debido a su economía de escala. Además, debido a la agrupación de edificios en una misma red, permitirán el desarrollo e implementación de algoritmos para la gestión de la energía en el sistema completo

multiFLEX: Flexible Multi-Utility, Multi-Service Smart Metering Architecture for Energy Vectors with Active Prosumers

In order to move forward the vision of Smart Grid, a flexible multi-utility and multi-service metering architecture is needed to allow innovative services and utilities for the different actors playing in this scenario. To achieve this, different meters (e.g. electric, water, heating and gas meters) must be integrated into a distributed architecture in order to gather and analyse heterogeneous data. Hence, such architecture provides in real-time a complete overview of the energy consumption and production in the grid from different prospectives. From customer viewpoint, this information can be used to provide user awareness and suggest green behaviours, thus reducing energy waste. From energy operator or utility provider viewpoint, for instance such analysis can: i) improve the demand response for optimizing the energy management during peak periods; ii) profile consumer energy behaviours for predicting the short term energy demand; iii) improve energy and market efficiency. In this paper, we discuss the characteristics of this infrastructure and its expected impacts on utility providers, energy operators and customers