A Guide for the Design of Benchmark Environments for Building Energy Optimization

The need for algorithms that optimize building energy consumption is usually motivated with the high energy consumption of buildings on a global scale. However, the current practice for evaluating the performance of such algorithms does not reflect this goal, as in most cases the performance is reported for one specific simulated building only, which provides no indication about the generalization of the score on other buildings. One approach to overcome this severe issue is to establish a shared collection of environments, each representing one simulated building setup, that would enable researchers to systematically compare and contrast the efficacy of their building optimization algorithms at scale. However, this requires that the individual environments are well designed for this goal. This paper is thus targeting the design of suitable environments for such a collection based on a detailed analysis of related publications that allows the identification of relevant characteristics for suitable environments. Based on this analysis a guide is developed that distills these characteristics into questions, intended to support a discussion of relevant topics during the design of such environments. Additional explanations and examples are provided for each question to make the guide more comprehensible. Finally, it is demonstrated how the guide can be applied, by utilizing it for the design of a novel environment, which represents an office building in tropical climate. This environment is released open source alongside this publication. We also indicate how test scenarios from existing publications could be enhanced to comply with the required characteristics according to our guide, underlining its importance for the future development and evaluation of building energy optimization algorithms, and thus for the sustainability of buildings in general

Robust occupancy inference with commodity WiFi

Accurate occupancy information of indoor environments is one of the key prerequisites for many pervasive and context-aware services, e.g. smart building/home systems. Some of the existing occupancy inference systems can achieve impressive accuracy, but they either require labour-intensive calibration phases, or need to install bespoke hardware such as CCTV cameras, which are privacy-intrusive by default. In this paper, we present the design and implementation of a practical end-to-end occupancy inference system, which requires minimum user effort, and is able to infer room-level occupancy accurately with commodity WiFi infrastructure. Depending on the needs of different occupancy information subscribers, our system is flexible enough to switch between snapshot estimation mode and continuous inference mode, to trade estimation accuracy for delay and communication cost. We evaluate the system on a hardware testbed deployed in a 600m 2 workspace with 25 occupants for 6 weeks. Experimental results show that the proposed system significantly outperforms competing systems in both inference accuracy and robustness

Extending sensor networks into the cloud using Amazon web services

Sensor networks provide a method of collecting environmental data for use in a variety of distributed applications. However, to date, limited support has been provided for the development of integrated environmental monitoring and modeling applications. Specifically, environmental dynamism makes it difficult to provide computational resources that are sufficient to deal with changing environmental conditions. This paper argues that the Cloud Computing model is a good fit with the dynamic computational requirements of environmental monitoring and modeling. We demonstrate that Amazon EC2 can meet the dynamic computational needs of environmental applications. We also demonstrate that EC2 can be integrated with existing sensor network technologies to offer an end-to-end environmental monitoring and modeling solution

Control of HVAC System via Implicit and Explicit MPC: an Experimental Case Study

Analysis and Implementation of different control strategy for an HVAC System. The thesis analyze two different approaches for the thermic control of building comparing them to simpler practice. In particular an Implicit MPC controller is implemented and studied, then, in order to reduce the online computation is designed and implementedope

Designing Artificial Neural Networks (ANNs) for Electrical Appliance Classification in Smart Energy Distribution Systems

En este proyecto se abordará el problema de la desagregación del consumo eléctrico a través del diseño de sistemas inteligentes, basados en redes neuronales profundas, que puedan formar parte de sistemas más amplios de gestión y distribución de energía. Durante la definición estará presente la búsqueda de una complejidad computacional adecuada que permita una implementación posterior de bajo costo. En concreto, estos sistemas realizarán el proceso de clasificación a partir de los cambios en la corriente eléctrica provocados por los distintos electrodomésticos. Para la evaluación y comparación de las diferentes propuestas se hará uso de la base de datos BLUED.This project will address the energy consumption disaggregation problem through the design of intelligent systems, based on deep artificial neural networks, which would be part of broader energy management and distribution systems. The search for adequate computational complexity that will allow a subsequent implementation of low cost will be present during algorithm definition. Specifically, these systems will carry out the classification process based on the changes caused by the different appliances in the electric current. For the evaluation and comparison of the different proposals, the BLUED database will be used.Máster Universitario en Ingeniería Industrial (M141

Model Predictive Control of HVAC Systems: Design and Implementation on a Real Case Study

The final aim of this work is to design, implement and test a controller on a real testbed kindly provided by KTH. The control paradigm presented in this thesis is a MPC that aims at saving energy as well as keeping the temperature and the CO2 concentration in a comfort range that guarantees the wellness of room occupants. To improve the knowledge of the plant, we also study the problem of modeling both the dynamics of of the system to be controlled and of the dedicated actuation syste

Model-predictive control for non-domestic buildings: a critical review and prospects

Model-predictive control (MPC) has recently excited a great deal of interest as a new control paradigm for non-domestic buildings. Since it is based on the notion of optimisation, MPC is, in principle, well-placed to deliver significant energy savings and reduction in carbon emissions compared to existing rule-based control systems. In this paper, we critically review the prospects for buildings MPC and, in particular, the central role of the predictive mathematical model that lies at its heart; our clear emphasis is on practical implementation rather than control-theoretic aspects, and covers the role of occupants as well as the form of the predictive model. The most appropriate structure for such a model is still an open question, which we discuss alongside the development of the initial model, and the process of updating the model during the building’s operational life. The importance of sensor placement is highlighted alongside the possibility of updating the model with occupants’ comfort perception. We conclude that there is an urgent need for research on the automated creation and updating of predictive models if MPC is to become an economically-viable control methodology for non-domestic buildings. Finally, more evidence through operating full scale buildings with MPC is required to demonstrate the viability of this method