Comprehensive evaluation of the structural characteristics of an urban metabolic system: Model development and a case study of Beijing

2013 ◽  
Vol 252 ◽  
pp. 106-113 ◽  
Author(s):  
Yan Zhang ◽  
Hong Liu ◽  
Bin Chen
Keyword(s):  
Comprehensive Evaluation ◽  
Structural Characteristics ◽  
Model Development ◽  
System Model ◽  
Metabolic System

scholarly journals A digital platform as a facilitator for assessing innovation potential and creating business models: a case study from the i3 project

2017 ◽  
Vol 11 (1) ◽  
pp. 982-993
Author(s):  
Francesco Bellini ◽  
Iana Dulskaia
Keyword(s):  
Business Model ◽  
Business Models ◽  
Social Acceptance ◽  
Comprehensive Evaluation ◽  
Model Development ◽  
Digital Platforms ◽  
Horizon 2020 ◽  
Innovation Potential ◽  
The Social

Abstract Many ideas flow into the innovation funnel but only 1 out 3000 becomes a successful new product. There are many variables that interact in this complex process and investors decisions are often based on experience and feeling rather than a comprehensive evaluation of the social, economic and technological factors. The innovation potential, the innovator capability, the accessibility of the technology as well as the social acceptance and the chosen business model are the some of the critical factors of a successful innovation strategy. In the broad sense, a business model is the approach of doing business through which a company can sustain itself and generate profits in the long term. Digital platforms can help manage and facilitate the complexity of value propositions and provide an immediate feedback to the entrepreneur. Creating value is necessary, but not sufficient, for an organization to profit from its business model. It is important to see the whole picture of the business that is why the business models are so important for a good start of the business. However, innovation assessment and business model development sometimes are not an easy task and ICT can make this process easier. Then, the aim of this paper is to explore the role of digital platforms as facilitators for the techno-socio-economic impact assessment and the development of sustainable business models through the analysis of a case study from the EU Horizon 2020 “i3 project”.

scholarly journals Predictive Maintenance in Building Facilities: A Machine Learning-Based Approach

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1044
Author(s):  
Yassine Bouabdallaoui ◽  
Zoubeir Lafhaj ◽  
Pascal Yim ◽  
Laure Ducoulombier ◽  
Belkacem Bennadji
Keyword(s):  
Machine Learning ◽  
Model Development ◽  
Predictive Maintenance ◽  
Data Availability ◽  
Machine Learning Techniques ◽  
Automation System ◽  
Building Maintenance ◽  
Collection Data ◽  
Iot Devices

The operation and maintenance of buildings has seen several advances in recent years. Multiple information and communication technology (ICT) solutions have been introduced to better manage building maintenance. However, maintenance practices in buildings remain less efficient and lead to significant energy waste. In this paper, a predictive maintenance framework based on machine learning techniques is proposed. This framework aims to provide guidelines to implement predictive maintenance for building installations. The framework is organised into five steps: data collection, data processing, model development, fault notification and model improvement. A sport facility was selected as a case study in this work to demonstrate the framework. Data were collected from different heating ventilation and air conditioning (HVAC) installations using Internet of Things (IoT) devices and a building automation system (BAS). Then, a deep learning model was used to predict failures. The case study showed the potential of this framework to predict failures. However, multiple obstacles and barriers were observed related to data availability and feedback collection. The overall results of this paper can help to provide guidelines for scientists and practitioners to implement predictive maintenance approaches in buildings.

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