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Abstract. This article describes the work being developed concerning the optimization of climate conditions in office buildings by the use of modeling and simulation tools to define the building's energy demand, and the design and implementation of PID controls for the different control areas of the HVAC system, whose parameters have been adjusted based on simulation. To perform these studies, different simulation tools have been combined to obtain a model of the entire system: design of building and building's energy demand in HVAC with Google SketchUp and EnergyPlus using a complete year of simulation with weather conditions of the building's location, and HVAC system modeling with Dymola. These different tools have been combined with BCVTB co-simulation platform. A physical implementation is being finished by the deployment of distributed temperature and humidity sensors along the building. The measured data will be used to fine tune the PID controller parameters previously designed in the modeling stage.

As the analysis of electrical loads is reaching data measured from low voltage power distribution networks, there is a need for the main agents involved in the operation and management of the power grids to segment the end users as a function of their shapes of daily energy consumption or load profiles, and to obtain patterns that allow to classify the users in groups based on how they consume the energy. However, this analysis is usually limited to the analysis of single days. Since the smart metering data are time series formed by sequential measurements of energy through each hour or quarter of hour of the day, and also through each day, thanks to the implementation of Advanced Metering Infrastructure (AMI) and the Smart Grid technologies, it becomes clear that the analysis of the data needs to be extended to consider the dynamic evolution of the consumption patterns through days, weeks, months, seasons, and even years. This is the objective of the present work. A new framework is presented that addresses the dynamic clustering, visualization and identification of temporal patterns in load profiles time series, fulfilling the detected gap in this area. The present development is a generic framework that allows the clustering and visualization of load profiles time series applying different classical clustering algorithms. A novel dynamic clustering algorithm is also presented, based on an initial segmentation of the energy consumption time series data in smaller surfaces, and the computation of a similarity measure among them applying the Hausdorff distance. Following, these developments are presented and tested on two dataset of energy consumption load profiles from a sample of residential users in Spain and London.

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