scholarly journals Application of Statistical and Artificial Intelligence Techniques for Medium-Term Electrical Energy Forecasting: A Case Study for a Regional Hospital

2020 ◽  
Vol 8 (3) ◽  
pp. 520-536 ◽  
Author(s):  
Oğuzhan Timur ◽  
Kasım Zor ◽  
Özgür Çelik ◽  
Ahmet Teke ◽  
Turgay İbrikçi
Keyword(s):  
Artificial Intelligence ◽  
Electrical Energy ◽  
Regional Hospital ◽  
Medium Term ◽  
Energy Forecasting ◽  
Artificial Intelligence Techniques

A Fuzzy Segmentation Approach to Guide Marketing Decisions

2012 ◽  
pp. 291-311
Author(s):  
Mònica Casabayó ◽  
Núria Agell
Keyword(s):  
Artificial Intelligence ◽  
Energy Industry ◽  
Real Case ◽  
Artificial Intelligence Techniques ◽  
Segmentation Approach ◽  
Fuzzy Segmentation ◽  
Realistic Information

The aim of this chapter is to present a fuzzy segmentation model that combines statistical and Artificial Intelligence techniques to identify and quantify multifaceted consumers. One of the primary challenges faced by companies is getting to know their consumers. The latter are increasingly complex, versatile, ever-changing, and even contradictory; in other words, they are multifaceted. There is thus a need for techniques and tools to be able to segment this type of consumer in order to provide companies with the realistic information they need to make the appropriate marketing decisions. A real case study from the Spanish energy industry is included in this chapter to demonstrate the potential of the segmentation model being proposed.

A Fuzzy Segmentation Approach to Guide Marketing Decisions

E-Marketing ◽  
2012 ◽  
pp. 368-388
Author(s):  
Mònica Casabayó ◽  
Núria Agell
Keyword(s):  
Artificial Intelligence ◽  
Energy Industry ◽  
Real Case ◽  
Artificial Intelligence Techniques ◽  
Segmentation Approach ◽  
Fuzzy Segmentation ◽  
Realistic Information

The aim of this chapter is to present a fuzzy segmentation model that combines statistical and Artificial Intelligence techniques to identify and quantify multifaceted consumers. One of the primary challenges faced by companies is getting to know their consumers. The latter are increasingly complex, versatile, ever-changing, and even contradictory; in other words, they are multifaceted. There is thus a need for techniques and tools to be able to segment this type of consumer in order to provide companies with the realistic information they need to make the appropriate marketing decisions. A real case study from the Spanish energy industry is included in this chapter to demonstrate the potential of the segmentation model being proposed.

Flood Mapping Using Remote Sensing Imagery and Artificial Intelligence Techniques: A Case Study in Brumadinho, Brazil

2019 ◽  
Vol 90 (sp1) ◽  
pp. 197
Author(s):  
Mutiara Syifa ◽  
Sung Jae Park ◽  
Arief Rizqiyanto Achmad ◽  
Chang-Wook Lee ◽  
Jinah Eom
Keyword(s):  
Artificial Intelligence ◽  
Remote Sensing ◽  
Flood Mapping ◽  
Remote Sensing Imagery ◽  
Artificial Intelligence Techniques

Wind Energy Forecasting with Artificial Intelligence Techniques: A Review

2020 ◽  
pp. 348-362 ◽  
Author(s):  
Jorge Maldonado-Correa ◽  
Marcelo Valdiviezo ◽  
Juan Solano ◽  
Marco Rojas ◽  
Carlos Samaniego-Ojeda
Keyword(s):  
Artificial Intelligence ◽  
Wind Energy ◽  
Energy Forecasting ◽  
Artificial Intelligence Techniques

scholarly journals An Approach to Share Self-Taught Knowledge between Home IoT Devices at the Edge

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 833 ◽  
Author(s):  
Ingook Jang ◽  
Donghun Lee ◽  
Jinchul Choi ◽  
Youngsung Son
Keyword(s):  
Artificial Intelligence ◽  
Internet Of Things ◽  
Power Consumption ◽  
Response Time ◽  
Knowledge Sharing ◽  
Smart Home ◽  
Artificial Intelligence Techniques ◽  
Iot Applications ◽  
Iot Devices

The traditional Internet of Things (IoT) paradigm has evolved towards intelligent IoT applications which exploit knowledge produced by IoT devices using artificial intelligence techniques. Knowledge sharing between IoT devices is a challenging issue in this trend. In this paper, we propose a Knowledge of Things (KoT) framework which enables sharing self-taught knowledge between IoT devices which require similar or identical knowledge without help from the cloud. The proposed KoT framework allows an IoT device to effectively produce, cumulate, and share its self-taught knowledge with other devices at the edge in the vicinity. This framework can alleviate behavioral repetition in users and computational redundancy in systems in intelligent IoT applications. To demonstrate the feasibility of the proposed concept, we examine a smart home case study and build a prototype of the KoT framework-based smart home system. Experimental results show that the proposed KoT framework reduces the response time to use intelligent IoT devices from a user’s perspective and the power consumption for compuation from a system’s perspective.

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