BP Neural Network Combination Prediction for Big Data Enterprise Energy Management System

2021 ◽  
Author(s):  
Sen Xu ◽  
Ryan Alturki ◽  
Ateeq Ur Rehman ◽  
Muhammad Usman Tariq
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Energy Management ◽  
Bp Neural Network ◽  
Management System ◽  
Energy Management System

Photovoltaics Energy Prediction Under Complex Conditions for a Predictive Energy Management System

2015 ◽  
Vol 137 (3) ◽  
Author(s):  
Martin Schmelas ◽  
Thomas Feldmann ◽  
Jesus da Costa Fernandes ◽  
Elmar Bollin
Keyword(s):  
Neural Network ◽  
Physical Model ◽  
Energy Management ◽  
Management System ◽  
Percentage Error ◽  
Positive Energy ◽  
Energy Management System ◽  
Energy Prediction ◽  
The Neural Network ◽  
Accurate Forecast

Solar energy converted and fed to the utility grid by photovoltaic modules has increased significantly over the last few years. This trend is expected to continue. Photovoltaics (PV) energy forecasts are thus becoming more and more important. In this paper, the PV energy forecasts are used for a predictive energy management system (PEMS) in a positive energy building. The publication focuses on the development and comparison of different models for daily PV energy prediction taking into account complex shading, caused for example by trees. Three different forecast methods are compared. These are a physical model with local shading measurements, a multilayer perceptron neural network (MLP), and a combination of the physical model and the neural network. The results show that the combination of the physical model and the neural network provides the most accurate forecast values and can improve adaptability. From April to December, the mean percentage error (MPE) of the MLP with physical information is 11.6%. From December to March, the accuracy of the PV predictions decreases to an MPE of 78.8%. This is caused by poorer irradiation forecasts, but mainly by snow coverage of the PV modules.

A smart home energy management system using IoT and big data analytics approach

2017 ◽  
Vol 63 (4) ◽  
pp. 426-434 ◽  
Author(s):  
A.R. Al-Ali ◽  
Imran A. Zualkernan ◽  
Mohammed Rashid ◽  
Ragini Gupta ◽  
Mazin Alikarar
Keyword(s):  
Big Data ◽  
Energy Management ◽  
Management System ◽  
Data Analytics ◽  
Smart Home ◽  
Big Data Analytics ◽  
Energy Management System ◽  
Home Energy Management ◽  
Home Energy Management System

scholarly journals IOT based Energy Management System for an Autonomous Hybrid Micro-Grid

2019 ◽  
Vol 8 (10) ◽  
pp. 1318-1323
Keyword(s):  
Big Data ◽  
Energy Management ◽  
Management System ◽  
Air Conditioning ◽  
Energy Utilization ◽  
Energy Management System ◽  
Industrial Sectors ◽  
Innovative Idea ◽  
Utilization Data ◽  
On Chip

Raising rate and require of power has led a lot of organizations to discover elegant ways for monitoring, controlling and reduction energy. To create an innovative idea to reduce the rate of energy consumption smart EMS (Energy Management System) is proposed in this paper. To develop IoT technologies and Big Data is used to improved hold energy utilization in commercial, housing and industrial sectors. An EMS is used to build smart homes is proposed for he developed cities. In this system, every residence tool is interfaced with a data attainment module that is an IoT object with an exclusive IP address ensuing in a huge mesh wireless network of devices. The data gaining SoC (System on Chip) module collects energy utilization data from every device of every stylish residence and send data to a centralized server for supplementary handing out and study. This information from all housing areas accumulates in the utility’s server as Big Data. EMS consumes off-the-shelf BI (Business Intelligence) and Big Data surveys software packages which improves the energy usages also to assemble user order. While air conditioning gives to 60% of power use in American countries, HVAC (Ventilation, Air Conditioning and Heating) are in use as a research to approve the proposed system.

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