Generación de modelo de control predictivo usando Matlab

Los Modelos de Control Predictivo (MCP) son alternativas prometedoras en la gestión eficiente de la energí­a y los recursos en las edificaciones. Crear un modelo de construcción preciso que sea lo suficientemente simple como para permitir que el problema de MCP resultante sea manejable es una tarea desafiante pero crucial en el desarrollo del control.En este artí­culo muestra el Modelado de Resistencia-Capacitancia para Edificios (MRCE) en Matlab Toolbox que facilita el modelado fí­sico de edificios. Toolbox proporciona un medio para la generación rápida de modelos de resistencia (capacitancia) lineal a partir de datos básicos de geometrí­a de edificios, construcción y sistemas. Además, admite la generación de los correspondientes costos y restricciones potencialmente variables en el tiempo. Toolbox se basa en principios de modelado previamente validados. En un estudio de caso, se generó automáticamente un modelo MRCE a partir de un archivo de datos de entrada EnergyPlus y se compararon sus capacidades predictivas con el modelo EnergyPlus. Los análisis energéticos en régimen estacionario en Matlab son tan precisos como los resultados generados en las herramientas computacionales destinadas exclusivamente a este propósito. La herramienta computacional Matlab se consolida en cada nueva versión como una plataforma más completa y óptima para el análisis ingenierí­a y de matemáticas aplicadas

A Comparison of Model Reduction Techniques for Multi-Zone Building Thermal Models

In this thesis model order reduction techniques are applied to a set of systems representing the thermodynamics of a multi-zone building. The models are intended to be used in a model predictive control (MPC) application, with the individual zones defined using a simplified two temperature model. There has been an increased interest in model identification and reduction for MPC applications of building models that include multi-zone and non-linear models, but the most of this work has focused on models where individual zones are represented with a higher order model. Manual pole/zero removal, dominant eigenvalue, and balanced model reduction methods are presented, along with a proposed application specific method that takes advantage of the zone model’s simplified form. The proposed method treats a set of the zones as a common airspace with comparable control and reduces the underlying resistor/capacitor (RC) network. These methods are applied to a two zone and a six zone model with various coupling configuration tested. The general form of the multi-zone model proves difficult to reduce without making modification to the original form. The effects of reducing the inputs and outputs, through methods such as using a common temperature setpoint, are presented with significant improvements to reduction capabilities. Balanced model reduction the 18th order system down to 9th and 5th based on which inputs and outputs are reduced, and the application specific methods is able to reduce the same system down to a 3rd order model when the inputs and outputs are fully reduced

Artificial Intelligence and Machine Learning Approaches to Energy Demand-Side Response: A Systematic Review

Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility, and hence improve the reliability of energy systems in a cost-effective way. Yet, the high complexity of the tasks associated with DR, combined with their use of large-scale data and the frequent need for near real-time de-cisions, means that Artificial Intelligence (AI) and Machine Learning (ML) — a branch of AI — have recently emerged as key technologies for enabling demand-side response. AI methods can be used to tackle various challenges, ranging from selecting the optimal set of consumers to respond, learning their attributes and pref-erences, dynamic pricing, scheduling and control of devices, learning how to incentivise participants in the DR schemes and how to reward them in a fair and economically efficient way. This work provides an overview of AI methods utilised for DR applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects. The papers are classified with regards to both the AI/ML algorithm(s) used and the application area in energy DR. Next, commercial initiatives are presented (including both start-ups and established companies) and large-scale innovation projects, where AI methods have been used for energy DR. The paper concludes with a discussion of advantages and potential limitations of reviewed AI techniques for different DR tasks, and outlines directions for future research in this fast-growing area