• Energy Research

Factories use many manufacturing processes that consume a lot of energy and highly contribute to greenhouse gas emissions. The introduction of the concept of Industrial Internet in USA and Industry 4.0 in Europe offers many opportunities to reduce energy consumption in these factories. Introducing and utilizing smart techniques for the applications pertinent to manufacturing processes within the Industry 4.0 domain can offer many benefits for reducing energy consumption in smart factories. This paper investigates and discusses these opportunities and benefits. This paper also discusses the roles of Industry 4.0 technologies in enabling these opportunities. Consequently, introducing these capabilities will help significantly reduce both production costs and greenhouse gas emissions. This paper then provides a benefit analysis that shows the advantages of such leverage. In addition, this paper offers an enabling architecture and its components that include a cyber-physical system manufacturing services' layer, a fog manufacturing services' layer, a cloud manufacturing services' layer, and a blockchain-based service-oriented middleware to support such opportunities.

The potential for saving on energy related cost with timely and accurate Fault Detection and Diagnosis (FDD) in Building Management Systems (BMS) has been estimated to ca. 15–30% of the overall building energy-related cost. Due to the expansion of well-equipped smart buildings that feature multitudes of sensors and meters that enable collection of large amounts of data, FDD data-based methods have become very popular. Sensor and meter data, however, have been found as insufficient for FDD purposes, and it needs to be complemented with event/fault data that is more difficult to obtain. To account for the unavailability of event/fault data, BMS can benefit from sharing each other's data and utilize it for collaborative FDD. To support collaborative FDD and sharing of data, we rely on cloud computing. In this paper we present a framework for collaborative FDD of smart buildings that utilizes cloud computing and enables BMS to share and benefit from each other's data.

Existing commercial buildings represent a challenge in the energy efficiency domain. Energy efficiency of a building, very often equalized to a building;s performance should not be observed as a standalone issue. For commercial buildings, energy efficiency needs to be observed and assessed within the context of performance of resident businesses. We examine both business performance and energy performance and how they relate to one another to conclude that building occupants, who are also employees, hold the key to optimizing both metrics in one of the most cost-efficient ways. Finally, the goal of our contribution is twofold: 1) to re-scope the concept of building performance to and show the importance to consider, hand-in-hand, both energy performance and performance of resident businesses, and 2) re-state the importance of the potential that lies in the active involvement of building occupants in optimizing overall building performance.

A significant proportion of energy consumption by buildings worldwide, estimated to ca. 40%, has yielded a high importance to studying buildings’ performance. Performance Testing is a mean by which buildings can be continuously commissioned to ensure that they operate as designed. Historically, setup of Performance Tests has been manual and labor-intensive and has required intimate knowledge of buildings’ complexity and systems. The emergence of the concept of smart buildings has provided an opportunity to overcome this restriction. In this paper, we propose a framework for automated Performance Testing of smart buildings that utilizes metadata models. The approach features automatic detection of applicable Performance Tests using metadata queries and their corresponding instantiation, as well as continuous commissioning based on metadata. The presented approach has been implemented and tested on a case study building at a university campus in Denmark.

Abstract Buildings account for ca. 40% of the total energy consumption and ca. 20% of the total CO2 emissions. More effective and advanced control integrated into Building Management Systems (BMS) represents an opportunity to improve energy efficiency. The ease of availability of sensors technology and instrumentation within today's intelligent buildings enable collecting high quality data which could be used directly in data-based analysis and control methods. The area of data-based systems analysis and control is concentrating on developing analysis and control methods that rely on data collected from meters and sensors, and information obtained by data processing. This differs from the traditional model-based approaches that are based on mathematical models of systems. We propose and describe a data-driven controllability measure for discrete-time linear systems. The concept is developed within a data-based system analysis and control framework. Therefore, only measured data is used to obtain the proposed controllability measure. The proposed controllability measure not only shows if the system is controllable or not, but also reveals the level of controllability, which is the information its previous counterparts failed to provide. We use two illustrative examples to demonstrate the method, which also include an intelligent building.

Energy use of buildings represents roughly 40% of the overall energy consumption. Most of the national agendas contain goals related to reducing the energy consumption and carbon footprint. Timely and accurate fault detection and diagnosis (FDD) in building management systems (BMS) have the potential to reduce energy consumption cost by approximately 15-30%. Most of the FDD methods are data-based, meaning that their performance is tightly linked to the quality and availability of relevant data. Based on our experience, faults and relevant events data is very sparse and inadequate, mostly because of the lack of will and incentive for those that would need to keep track of faults. In this paper we introduce the idea of using crowdsourcing to support FDD data collection processes, and illustrate our idea through a mobile application that has been implemented for this purpose. Furthermore, we propose a strategy of how to successfully deploy this building occupants' crowdsourcing application.

The rapid expansion of industrialization and the increase in energy demand make energy resource management necessary in various industries. Creating digital twins from available streaming data offers a practical way to enhance the efficiency of manufacturing systems. Digital twins allow for detailed analysis of key metrics, such as the energy consumption of complete manufacturing systems. Utilizing digital twins can greatly assist in improving manufacturing processes for reduced energy consumption. In this paper, we investigate the data requirements for the generation of energy-oriented digital twins of manufacturing systems. For this purpose, we developed a case study to illustrate and outline the necessary data requirements for a manufacturing system to enable the collection of data that can be used to extract energy-oriented simulation models. We, subsequently, analyze and generalize our findings that can be applied to a broader context.

Livestock farming plays a key role in the global food system, the economy, and the livelihood of millions of people. One of the undesirable facts in livestock production is the still insufficient consideration of the related environmental sustainability. Globally, livestock production accounts for about 14.5% of total greenhouse gas (GHG) emissions. These emissions are significantly changing our atmosphere and their impact is increasing. The main causes of these emissions are inefficient land resource management, synthetic fertilizer application, enteric fermentation, manure-related, and animal breed. It is, therefore, important to apply appropriate farm planning and management strategies to reduce the emissions. To support the global effort, we developed an application that we introduce in this paper, FarmMOODSS, to estimate net emissions by calculating GHG emissions at the farm level. FarmMOODSS recommends an optimized feeding program for emission mitigation. A multi-objective optimization algorithm is applied for feed optimization, including an IoT data mining function. The tool is developed based on a dairy farm use case.