513 research outputs found
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iSEA: IoT-based smartphone energy assistant for prompting energy-aware behaviors in commercial buildings
Providing personalized energy-use information to individual occupants enables the adoption of energy-aware behaviors in commercial buildings. However, the implementation of individualized feedback still remains challenging due to the difficulties in collecting personalized data, tracking personal behaviors, and delivering personalized tailored information to individual occupants. Nowadays, the Internet of Things (IoT) technologies are used in a variety of applications including real-time monitoring, control, and decision-making due to the flexibility of these technologies for fusing different data streams. In this paper, we propose a novel IoT-based smartphone energy assistant (iSEA) framework which prompts energy-aware behaviors in commercial buildings. iSEA tracks individual occupants through tracking their smartphones, uses a deep learning approach to identify their energy usage, and delivers personalized tailored feedback to impact their usage. iSEA particularly uses an energy-use efficiency index (EEI) to understand behaviors and categorize them into efficient and inefficient behaviors. The iSEA architecture includes four layers: physical, cloud, service, and communication. The results of implementing iSEA in a commercial building with ten occupants over a twelve-week duration demonstrate the validity of this approach in enhancing individualized energy-use behaviors. An average of 34% energy savings was measured by tracking occupants’ EEI by the end of the experimental period. In addition, the results demonstrate that commercial building occupants often ignore controlling over lighting systems at their departure events that leads to wasting energy during non-working hours. By utilizing the existing IoT devices in commercial buildings, iSEA significantly contributes to support research efforts into sensing and enhancing energy-aware behaviors at minimal costs
Demand forecasting model for load shifting strategy in building energy management system
Among the sectors with the highest energy consumption are transport, industries, and buildings. Buildings are responsible for the third part of energy consumption and almost 40% of CO2 emissions worldwide. The search to improve the comfort of the occupants inside the buildings has brought a consequence that buildings are increasingly equipped with devices that help to improve the thermal comfort, visual comfort, and air quality inside the buildings, causing more energy demand regardless of the type of building making buildings an untapped efficiency potential.This doctoral thesis presents a model for forecasting electricity demand in buildings based on machine learning for load-shifting strategies, which can be implemented in building energy management systems. First, the state of the art of building energy management systems is analyzed, as well as the different management strategies used within these systems. Second, within the predictive control model management strategy, the forecast models of energy consumption in buildings are analyzed, as well as the methods, input variables, prediction horizon, and metrics. Finally, about the analysis carried out on the energy consumption forecasting models, a short-term energy consumption forecasting strategy based on machine learning is developed that allows forecasting the demand for the next 24 hours from any time of the previous day.Dentro de los sectores de mayor consumo energético se encuentran: el transporte, las industrias y los edificios. Siendo los edificios responsables de una tercera parte del consumo de energía y casi un 40% de las emisiones de CO2 a nivel mundial. La búsqueda por mejorar el confort de los
ocupantes dentro de los edificios ha traído como consecuencia que los edificios estén cada vez más equipados con dispositivos que ayudan a mejorar el confort térmico, el confort visual y la calidad de aire dentro de los edificios. Causando que cada vez más la demanda de energía de los
edificios independientemente del tipo de edificio que sean se encuentre en crecimiento y haciendo que los edificios sean un potencial de eficiencia sin explotar. Esta tesis doctoral presenta un modelo para pronóstico de la demanda eléctrica en edificios basado en aprendizaje automático para estrategias de desplazamiento de cargas, el cual puede ser implementado en sistemas de gestión energética para edificios. En primer lugar, se analiza el estado del arte de los sistemas de gestión energética de edificios, así como las diferentes estrategias de gestión que se utilizan dentro de estos sistemas. En segundo lugar, dentro de la estrategia de gestión de modelo de control predictivo se analiza los modelos de pronóstico de consumo de energía en edificios, así como los métodos, variables de entrada, horizonte de predicción y métricas. Por último, en referencia al análisis realizado sobre los modelos de pronóstico de consumo de energía se desarrolla una estrategia de pronóstico de demanda a corto plazo basada en aprendizaje
automático que permite pronosticar la demanda de las próximas 24 horas a partir de cualquier hora del día anterior.Escuela de DoctoradoDoctorado en Ingeniería Industria
A systematic literature review on the use of artificial intelligence in energy self-management in smart buildings
Buildings are one of the main consumers of energy in cities, which is why a lot of research has been generated around this problem. Especially, the buildings energy management systems must improve in the next years. Artificial intelligence techniques are playing and will play a fundamental role in these improvements. This work presents a systematic review of the literature on researches that have been done in recent years to improve energy management systems for smart building using artificial intelligence techniques. An originality of the work is that they are grouped according to the concept of "Autonomous Cycles of Data Analysis Tasks", which defines that an autonomous management system requires specialized tasks, such as monitoring, analysis, and decision-making tasks for reaching objectives in the environment, like improve the energy efficiency. This organization of the work allows us to establish not only the positioning of the researches, but also, the visualization of the current challenges and opportunities in each domain. We have identified that many types of researches are in the domain of decision-making (a large majority on optimization and control tasks), and defined potential projects related to the development of autonomous cycles of data analysis tasks, feature engineering, or multi-agent systems, among others.European Commissio
Usability evaluation of a web-based tool for supporting holistic building energy management
This paper presents the evaluation of the level of usability of an intelligent monitoring and control interface for energy efficient management of public buildings, called BuildVis, which forms part of a Building Energy Management System (BEMS.) The BEMS ‘intelligence’ is derived from an intelligent algorithm component which brings together ANN-GA rule generation, a fuzzy rule selection engine, and a semantic knowledge base. The knowledge base makes use of linked data and an integrated ontology to uplift heterogeneous data sources relevant to building energy consumption. The developed ontology is based upon the Industry Foundation Classes (IFC), which is a Building Information Modelling (BIM) standard and consists of two different types of rule model to control and manage the buildings adaptively. The populated rules are a mix of an intelligent rule generation approach using Artificial Neural Network (ANN) and Genetic Algorithms (GA), and also data mining rules using Decision Tree techniques on historical data. The resulting rules are triggered by the intelligent controller, which processes available sensor measurements in the building. This generates ‘suggestions’ which are presented to the Facility Manager (FM) on the BuildVis web-based interface. BuildVis uses HTML5 innovations to visualise a 3D interactive model of the building that is accessible over a wide range of desktop and mobile platforms. The suggestions are presented on a zone by zone basis, alerting them to potential energy saving actions. As the usability of the system is seen as a key determinate to success, the paper evaluates the level of usability for both a set of technical users and also the FMs for five European buildings, providing analysis and lessons learned from the approach taken
Developing of an Offline Monitoring Method for the Energy Demand of a Healthcare Facility in Italy
Hospitals are among the most energy-intensive buildings in the service industry. The development of energy management strategies could lead to important energy savings, and it must pass through detailed analyses of each specific activity energy requirement. The present study aims to find the main energy drivers of a healthcare facility and to develop an offline monitoring method appliable to future healthcare energy requirements. A Multiple Linear Regression model has been realized to define the standard energy consumption based on the year 2019, allowing to realize a Cumulative Sum of differences control chart including the 2020 energy consumption data. The proposed method allows to find variations between actual and standard building energy demands, being a useful tool to monitor the effectiveness of energy system control strategies
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