scholarly journals Complementary grid power prediction using artificial neural network in the energy management system of a disaster prevention smart solar microgrid

2020 ◽  
pp. 879-889
Author(s):  
Thanh Phuong Nguyen ◽  
Chao-Tsung Yeh ◽  
Ming-Yuan Cho ◽  
Yao-Ting Huang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Management ◽  
Management System ◽  
Energy Management System ◽  
Disaster Prevention ◽  
Power Prediction ◽  
Artificial Neural

scholarly journals A NILM method for cooling load disaggregation based on artificial neural network

2019 ◽  
Vol 111 ◽  
pp. 05020 ◽  
Author(s):  
Ziwei Xiao ◽  
Jiaqi Yuan ◽  
Wenjie Gang ◽  
Chong Zhang ◽  
Xinhua Xu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Management ◽  
Residential Buildings ◽  
Building Energy ◽  
Cooling Load ◽  
Building Energy Management ◽  
Load Disaggregation ◽  
Artificial Neural ◽  
Cooling Loads

The demand of building energy management has increased due to high energy saving potentials. Load monitor and disaggregation can provide useful information for building energy management systems with detailed and individual loads of the building, so corresponding energy efficient measures can be taken to reduce the energy consumption of buildings. The technique is investigated widely in residential buildings known as Non-Intrusive Load Monitoring (NILM). However, relevant studies are not sufficient for non-residential buildings, especially for the cooling loads. This paper proposes a NILM method for cooling load disaggregation using artificial neural network. The cooling load is disaggregated into four categories: building envelope load, occupant load, equipment load and fresh air load. Two approaches are used to realize the load disaggregation: one is based on the Fourier transfer of the cooling loads, the other takes the cooling load, dry-bulb temperature and humidity of outdoor air, and time as inputs. By implementing the methods in a metro station, the performance of the proposed method can be obtained. Results show that both approaches can realize the load disaggregation accurately, with a RMSE less than 11.2. The second approach is recommended with a higher accuracy.

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.

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