Long- and Short-Term Approaches for Power Consumption Prediction Using Neural Networks

2020 ◽  
pp. 219-236
Author(s):  
Juan Carlos Morales ◽  
Salvador Moreno ◽  
Carlos Bailón ◽  
Héctor Pomares ◽  
Ignacio Rojas ◽  
...  
Keyword(s):  
Neural Networks ◽  
Power Consumption ◽  
Short Term ◽  
Power Consumption Prediction ◽  
Consumption Prediction

Pre-execution power consumption prediction of computational multithreaded workloads

2014 ◽  
Vol 17 (4) ◽  
pp. 1323-1333 ◽  
Author(s):  
Hamid Fadishei ◽  
Hossein Deldari ◽  
Mahmoud Naghibzadeh
Keyword(s):  
Power Consumption ◽  
Multithreaded Workloads ◽  
Power Consumption Prediction ◽  
Consumption Prediction

scholarly journals Short-Term Electricity Consumption Forecasting Based on the EMD-Fbprophet-LSTM Method

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Guorong Zhu ◽  
Sha Peng ◽  
Yongchang Lao ◽  
Qichao Su ◽  
Qiujie Sun
Keyword(s):  
Time Series ◽  
Power Consumption ◽  
Prediction Model ◽  
Time Series Prediction ◽  
Electricity Consumption ◽  
Operating Efficiency ◽  
Short Term ◽  
Model Based ◽  
Consumption Data ◽  
Consumption Prediction

Short-term electricity consumption data reflects the operating efficiency of grid companies, and accurate forecasting of electricity consumption helps to achieve refined electricity consumption planning and improve transmission and distribution transportation efficiency. In view of the fact that the power consumption data is nonstationary, nonlinear, and greatly influenced by the season, holidays, and other factors, this paper adopts a time-series prediction model based on the EMD-Fbprophet-LSTM method to make short-term power consumption prediction for an enterprise's daily power consumption data. The EMD model was used to decompose the time series into a multisong intrinsic mode function (IMF) and a residual component, and then the Fbprophet method was used to predict the IMF component. The LSTM model is used to predict the short-term electricity consumption, and finally the prediction value of the combined model is measured based on the weights of the single Fbprophet and LSTM models. Compared with the single time-series prediction model, the time-series prediction model based on the EMD-Fbprophet-LSTM method has higher prediction accuracy and can effectively improve the accuracy of short-term regional electricity consumption prediction.

scholarly journals Power Consumption prediction of IoT application Protocols Based on Linear Regression

2021 ◽  
Vol 11 (2) ◽  
pp. 0-0
Keyword(s):  
Linear Regression ◽  
Power Consumption ◽  
Smart Grids ◽  
Smart Cities ◽  
Data Communications ◽  
Iot Applications ◽  
Access To Data ◽  
Application Protocols ◽  
Power Consumption Prediction ◽  
Consumption Prediction

The advent of the Internet of Things (IoT) augurs new cutting-edge applications in modern life such as smart cities and smart grids. These applications require protocols more efficient for ensuring the reliability of data communications in the IoT networks. Many works state that IoT cannot meet their demands without application protocols improvement with Artificial Intelligence (AI) as IoT are expected to generate unprecedented traffic giving IoT researchers access to data that can help in studying and analyzing the demands and develop application protocols conceptions to meet the requirement of IoT applications. In literature, several works introduced AI in some layers of the TCP/IP model including wireless communication and routing. In this article, an evaluation of application protocols HTTP, MQTT, DDS, XMPP, AMQP, and CoAP has been presented; and subsequently, the power consumption prediction of MQTT and COAP based on the linear regression model is analyzed, in order to enhance data communications in IoT applications.

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