Design of energy management system based on BP neural network

2019 ◽  
Author(s):  
Nan Li ◽  
Jinsheng Qi
Keyword(s):  
Neural Network ◽  
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.

Load Forecasting Model for Energy Management System using Elman Neural Network

2019 ◽  
Vol 1 (3) ◽  
pp. 48-56
Author(s):  
Sandhiya Devarajan ◽  
Chitra S
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Energy Management ◽  
Management System ◽  
Load Forecasting ◽  
Computational Time ◽  
Energy Management System ◽  
Electric Load ◽  
Elman Neural Network ◽  
Forecasting Accuracy

Electric load forecasting is used for forecasting of future electric loads. Since the economy and reliability of operations of a power system are greatly affected by electric load, cost savings mainly depend on load forecasting accuracy. An accurate system load forecasting which is used to calculate short-term electric load forecasts, is an essential component of any Energy Management System (EMS). This can be improved by making use of Artificial Neural Networks (ANN). Existing Boosted Neural Networks (BooNN) technique helps in reduction of forecasting errors and variation in forecasting accuracy. However it is not flexible to rapid load changes.In the proposed work, Elman Neural Network technique is considered. This technique improves the load forecasting accuracy. The proposed method is implemented in IEEE 14 bus system. Simulation results showed that this method has increased the Voltage profile and also the active power losses have been reduced. Overall power transfer capability has been improved. Also the computational time has been minimized when compared to the existing techniques.

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