scholarly journals An Effective Artificial Neural Network based Power Load Prediction Algorithm

2019 ◽  
Vol 178 (20) ◽  
pp. 35-41
Author(s):  
Shoeb Mohammad ◽  
Md. Khairul ◽  
Syed Refat
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prediction Algorithm ◽  
Load Prediction ◽  
Power Load ◽  
Artificial Neural

A Nonlinear Autoregressive With Exogenous Inputs Artificial Neural Network Model for Building Thermal Load Prediction

2019 ◽  
Vol 142 (5) ◽  
Author(s):  
Byeongho Yu ◽  
Dongsu Kim ◽  
Heejin Cho ◽  
Pedro Mago
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Nonlinear System ◽  
Prediction Model ◽  
Thermal Load ◽  
Ann Model ◽  
Load Prediction ◽  
Artificial Neural ◽  
Nonlinear Autoregressive

Abstract Thermal load prediction is a key part of energy system management and control in buildings, and its accuracy plays a critical role to improve building energy performance and efficiency. Regarding thermal load prediction, various types of prediction model have been considered and studied, such as physics-based, statistical, and machine learning models. Physical models can be accurate but require extended lead time for model development. Statistical models are relatively simple to develop and require less computation time, but they may not provide accurate results for complex energy systems with intricate nonlinear dynamic behaviors. This study proposes an artificial neural network (ANN) model, one of the prevalent machine learning methods to predict building thermal load, combining with the concept of nonlinear autoregressive with exogenous inputs (NARX). NARX-ANN prediction model is distinguished from typical ANN models because the NARX concept can address nonlinear system behaviors effectively based on its recurrent architectures and time indexing features. To examine the suitability and validity of NARX-ANN model for building thermal load prediction, a case study is carried out using the field data of an academic campus building at Mississippi State University (MSU). Results show that the proposed NARX-ANN model can provide an accurate and robust prediction performance and effectively address nonlinear system behaviors in the prediction.

scholarly journals Optimization of Load Forecasting in Smartgrid using Artificial Neural Network based NFTOOL and NNTOOL

2022 ◽  
Vol 2161 (1) ◽  
pp. 012068
Author(s):  
Sthitprajna Mishra ◽  
Bibhu Prasad Ganthia ◽  
Abel Sridharan ◽  
P Rajakumar ◽  
D. Padmapriya ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Smart Grid ◽  
User Interfaces ◽  
Load Forecasting ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Load Prediction ◽  
Artificial Neural ◽  
Optimal Reserve

Abstract The motivation behind the research is the requirement of error-free load prediction for the power industries in India to assist the planners for making important decisions on unit commitments, energy trading, system security & reliability and optimal reserve capacity. The objective is to produce a desktop version of personal computer based complete expert system which can be used to forecast the future load of a smart grid. Using MATLAB, we can provide adequate user interfaces in graphical user interfaces. This paper devotes study of load forecasting in smart grid, detailed study of architecture and configuration of Artificial Neural Network(ANN), Mathematical modeling and implementation of ANN using MATLAB and Detailed study of load forecasting using back propagation algorithm.

scholarly journals A hybrid artificial neural network - genetic algorithm for load shedding

2020 ◽  
Vol 10 (3) ◽  
pp. 2250 ◽  
Author(s):  
Le Trong Nghia ◽  
Quyen Huy Anh ◽  
Phung Trieu Tan ◽  
N Thai An
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Load Shedding ◽  
Software Systems ◽  
Training Time ◽  
Power Load ◽  
Artificial Neural ◽  
Input Variables

This paper proposes the method of applying Artificial Neural Network (ANN) with Back Propagation (BP) algorithm in combination or hybrid with Genetic Algorithm (GA) to propose load shedding strategies in the power system. The Genetic Algorithm is used to support the training of Back Propagation Neural Networks (BPNN) to improve regression ability, minimize errors and reduce the training time. Besides, the Relief algorithm is used to reduce the number of input variables of the neural network. The minimum load shedding with consideration of the primary and secondary control is calculated to restore the frequency of the electrical system. The distribution of power load shedding at each load bus of the system based on the phase electrical distance between the outage generator and the load buses. The simulation results have been verified through using MATLAB and PowerWorld software systems. The results show that the Hybrid Gen-Bayesian algorithm (GA-Trainbr) has a remarkable superiority in accuracy as well as training time. The effectiveness of the proposed method is tested on the IEEE 37 bus 9 generators standard system diagram showing the effectiveness of the proposed method.

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