• Energy Research

This paper explores the viability of electric vehicle charging point operators to act as flexibility service providers in local flexibility markets. The work focuses on the requirements for operating in local intra-day markets and specifically in solving grid congestion at the distribution level. The explored approach assumes an alternative to bilateral agreements constrained to the capacity of the charging point operator to forecast the electric vehicle demand and flexibility effectively.The current paper analyses the flexibility capacity and proposes a methodology to address the re-dispatch process within the GOPACS (The Netherlands) context.The flexibility estimation methodology comprises two forecasting steps: forecasting the aggregated flexibility capacity and forecasting electric vehicles flexibility. A detailed case study presents data from the real electric vehicle sessions in Amsterdam City. The experimental results validate the effectiveness of the proposed methodology, establishing a robust basis for further research.

The electric grid is evolving. Smart grids and demand response systems will increase the performance of the grid in terms of cost efficiency, resilience and safety. Accurate load forecasting is an important issue in the daily operation and control of a power system. A suitable short term load forecasting will enable a utility provider to plan the resources and also to take control measures to balance the supply and demand of electricity. The aim of this paper is to create a method to forecast the electric load in a non-residential building. Another goal is to analyse what kind of data, as weather, indoor ambient, calendar and building occupancy, is the most relevant in building load forecasting. A simple method, tested with three different models, such as MLR, MLP and SVR, is proposed. The results, from a real case study in the University of Girona, show that the proposed forecast method has high accuracy and low computational cost This research has been partially supported by the Spanish Government project MESC (Ref. DPI2013-47450-C2-1-R). Also we would like to thank the Department of Physics and acknowledge the technical assistance and maintenance service of the UdG (SOTIM) which provided the weather and consumption data respectively. The authors belong to the ‘Smart IT Engineering and Solutions’ accredited research group (Generalitat de Catalunya, 2014 SGR 1052)

This work is focused on the data based modelling and monitoring of a family of modular systems that have multiple replicated structures with the same nominal variables and show temporal behaviour with certain periodicity. These characteristics are present in many systems in numerous fields such as the construction or energy sector or in industry. The challenge for these systems is to be able to exploit the redundancy in both time and the physical structure. In this paper the authors present a method for representing such granular systems using N-dimensional data arrays which are then transformed into the suitable 2-dimensional matrices required to perform statistical processing. Here, the focus is on pre-processing data using a non-unique folding-unfolding algorithm in a way that allows for different statistical models to be built in accordance with the monitoring requirements selected. Principal Component Analysis (PCA) is assumed as the underlying principle to carry out the monitoring. Thus, the method extends the Unfold Principal Component Analysis (Unfold-PCA or Multiway PCA), applied to 3D arrays, to deal with N-dimensional matrices. However, this method is general enough to be applied in other multivariate monitoring strategies. Two of examples in the area of energy efficiency illustrate the application of the method for modelling. Both examples illustrate how when a unique data-set folded and unfolded in different ways, it offers different modelling capabilities. Moreover, one of the examples is extended to exploit real data. In this case, real data collected over a two-year period from a multi-housing social-building located in down town Barcelona (Catalonia) has been used This work has been carried out by the research group eXIT (http://exit.udg.edu), funded through the following projects: MESC project(Ref. DPI2013-47450-C21-R) and its continuation CROWDSAVING (Ref.TIN2016-79726-C2-2-R), both funded by the Spanish Ministerio de Industria y Competitividad within the Research, Development and Innovation Program oriented towards the Societal Challenges, and also the project Hit2Gap of the Horizon 2020 research and innovation program under grant agreement N680708. The author Llorenç Burgas would also like to thank Girona University for their support through the competitive grant for doctoral formation IFUdG2016

Abstract This work presents the development of a web service module for monitoring energy consumption data recorded in buildings. This software is based on the application of Multivariate Principal Component Analysis (MPCA) to implement a fault detection and diagnosis tool that detects anomalies or misbehaviors in energy consumptions. The result can be integrated into an architecture to facilitate the access and management of data coming from different energy subsystems (and other sources of data as meteorological information) involved in the operation of a building.

This paper presents a complete methodology, together with its implementation as a web application, for monitoring smart buildings. The approach uses unfold-Principal Component Analysis (unfold-PCA) as a batch projection method and two statistics, Hotelling’s T-squared (T2) and the squared prediction error (SPE), for alarm generation resulting in two simple control charts independently on the number of variables involved. The method consists of modelling the normal operating conditions of a building (entire building, room or subsystem) with latent variables described expressing the principal components. Thus, the method allows detecting faults and misbehaviour as a deviation of previously mentioned statistics from their statistical thresholds. Once a fault or misbehaviour is detected, the isolation of sensors that mostly contribute to such detection is proposed as a first step for diagnosis. The methodology has been implemented under a SaaS (software as a service) approach to be offered to multiple buildings as an on-line application for facility managers. The application is general enough to be used for monitoring complete buildings, or parts of them, using on-line data. A complete example of use for monitoring the performance of the air handling unit of a lecture theatre is presented as demonstrative example and results are discussed

Heating, ventilation, and air conditioning (HVAC) systems account for up to 40% of the total energy consumption in buildings. Improving the modeling of HVAC components is necessary to optimize energy efficiency, maintain indoor thermal comfort, and reduce their carbon footprint. This work addresses the lack of a general methodology for data preprocessing by introducing a novel approach for feature extraction and feature selection based on physical equations and expert knowledge that can be applied to any data-driven model. The proposed framework enables the forecasting of indoor temperatures and the energy consumption of individual HVAC components. The methodology is validated with real-world data from a system involving a fan coil unit and a thermal inertia deposit powered by geothermal energy, achieving a coefficient of determination (R2) of 0.98 and mean absolute percentage error (MAPE) of 0.44%.

AbstractIn this paper Principal Component Analysis (PCA) is proposed for monitoring electric consumption of building. PCA allows modeling correlations between independent variables (weather, calendar) and energy consumption at different time scales (hourly, daily, weekly monthly). Multiway principal component analysis (MPCA) is used to model time dependencies of variables as it is commonly done in batch process monitoring. This approach allows defining simple statistic indices T2 and SPE to be used in monitoring charts. These indices are used to detect abnormal behaviours at selected time scales. After detection, contribution analysis is performed to isolate variables responsible of such misbehaviour. Exploitation of such models, obtained during normal operating conditions, can be used to detect both faults in sensors and misbehaviours in consumption patterns with respect to independent variables. The paper presents the methodology and illustrates it in a case study focused on academic buildings situated in the Campus of the University of Girona.