The Research on M2M Load Prediction Algorithm

2018 ◽  
Author(s):  
Chaofeng Fu ◽  
Ningbo Zhang ◽  
Guixia Kang ◽  
Yifan Zhang
Keyword(s):  
Prediction Algorithm ◽  
Load Prediction

scholarly journals District Heating Load Prediction Algorithm Based on Feature Fusion LSTM Model

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2122 ◽  
Author(s):  
Guixiang Xue ◽  
Yu Pan ◽  
Tao Lin ◽  
Jiancai Song ◽  
Chengying Qi ◽  
...  
Keyword(s):  
Prediction Accuracy ◽  
Feature Fusion ◽  
District Heating ◽  
Accurate Prediction ◽  
Prediction Algorithm ◽  
Load Prediction ◽  
Short Term ◽  
District Heating System ◽  
Heating Load ◽  
The Impact

The smart district heating system (SDHS) is an important element of the construction of smart cities in Northern China; it plays a significant role in meeting heating requirements and green energy saving in winter. Various Internet of Things (IoT) sensors and wireless transmission technologies are applied to monitor data in real-time and to form a historical database. The accurate prediction of heating loads based on massive historical datasets is the necessary condition and key basis for formulating an optimal heating control strategy in the SDHS, which contributes to the reduction in the consumption of energy and the improvement in the energy dispatching efficiency and accuracy. In order to achieve the high prediction accuracy of SDHS and to improve the representation ability of multi-time-scale features, a novel short-term heating load prediction algorithm based on a feature fusion long short-term memory (LSTM) model (FFLSTM) is proposed. Three characteristics, namely proximity, periodicity, and trend, are found after analyzing the heating load data from the aspect of the hourly time dimension. In order to comprehensively utilize the data’s intrinsic characteristics, three LSTM models are employed to make separate predictions, and, then, the prediction results based on internal features and other external features at the corresponding moments are imported into the high-level LSTM model for fusion processing, which brings a more accurate prediction result of the heating load. Detailed comparisons between the proposed FFLSTM algorithm and the-state-of-art algorithms are conducted in this paper. The experimental results show that the proposed FFLSTM algorithm outperforms others and can obtain a higher prediction accuracy. Furthermore, the impact of selecting different parameters of the FFLSTM model is also studied thoroughly.

An Infrastructure-as-a-Service Cloud

2013 ◽  
pp. 302-324 ◽  
Author(s):  
Weijia Song ◽  
Zhen Xiao
Keyword(s):  
Cloud Model ◽  
Scale Up ◽  
Resource Provisioning ◽  
Prediction Algorithm ◽  
Load Prediction ◽  
Accounting Model ◽  
Cloud Elasticity ◽  
Business Customers ◽  
Cloud Users ◽  
Service Cloud

Cloud computing allows business customers to elastically scale up and down their resource usage based on needs. This feature eliminates the dilemma of planning IT infrastructures for Cloud users, where under-provisioning compromises service quality while over-provisioning wastes investment as well as electricity. It offers virtually infinite resource. It also made the desirable “pay as you go” accounting model possible. The above touted gains in the Cloud model come from on-demand resource provisioning technology. In this chapter, the authors elaborate on such technologies incorporated in a real IaaS system to exemplify how Cloud elasticity is implemented. It involves the resource provisioning technologies in hypervisor, Virtual Machine (VM) migration scheduler and VM replication. The authors also investigate the load prediction algorithm for its significant impacts on resource allocation.

Data Transmission Mechanism Supporting Fairness and QoS in Wireless Sensor Networks

2013 ◽  
Vol 655-657 ◽  
pp. 1757-1760
Author(s):  
Seong Cheol Kim
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Transmission ◽  
Transmission Mechanism ◽  
Wireless Sensor ◽  
Prediction Algorithm ◽  
Load Prediction ◽  
Priority Level ◽  
Buffer Length

In this paper we propose a data transmission mechanism that supports fairness and Quality of Service (QoS) in a wireless sensor networks (WSNs). In this mechanism the received or measured data traffics will be assigned a priority level according to its transmission urgency. And the load prediction algorithm is used to support the fairness between different priority traffics. For this, the buffer length values of the nodes are continuously monitored for some period. Based on the buffer length variations for this period, the order of transmission is determined. FQSM also adapts cross-layer concept to rearrange the data transmission order in each sensor node's buffer, saves energy consumption by allowing few nodes in data transmission, and prolongs the network lifetime

scholarly journals Development of Fatigue Load Prediction Algorithm for Wind Turbines

2021 ◽  
Vol 41 (4) ◽  
pp. 93-105
Author(s):  
Jungchul Choi ◽  
Eunkuk Son ◽  
Gwangse Lee ◽  
Minsang Kang ◽  
Jinjae Lee ◽  
...  
Keyword(s):  
Wind Turbines ◽  
Prediction Algorithm ◽  
Load Prediction ◽  
Fatigue Load
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