scholarly journals Monthly Maximum load Demand Forecasting for Sulaimani Governorate Using Different Weather Conditions Based on Artificial Neural Network Model

2020 ◽  
Vol 4 (2) ◽  
pp. 10
Author(s):  
Najat Hassan Abdulkareem
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Demand Forecasting ◽  
Peak Load ◽  
Weather Conditions ◽  
Maximum Load ◽  
Percentage Error ◽  
Load Demand ◽  
North Iraq ◽  
Artificial Neural

Medium-term forecasting is an important category of electric load forecasting that covers a time span of up to 1 year ahead. It suits outage and maintenance planning, as well as load switching operation. There is an on-going attention toward putting new approaches to the task. Recently, artificial neural network has played a successful role in various applications. This paper is presents a monthly peak load demand forecasting for Sulaimani (located in North Iraq) using the most widely used traditional method based on an artificial natural network, the performance of the model is tested on the actual historical monthly demand of the governorate for the years 2014–2018. The standard mean absolute percentage error (MAPE) method is used to evaluate the accuracy of forecasting models, the results obtained show a very good estimation of the load. The MAPE is 0.056.

scholarly journals Data Preprocessing for Load Forecasting using Artificial Neural Network

2020 ◽  
Author(s):  
Rafael S. F. Ferraz ◽  
Renato S. F. Ferraz ◽  
Lucas F. S. Azeredo ◽  
Benemar A. de Souza
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power System ◽  
Demand Forecasting ◽  
Energy Resource ◽  
Data Preprocessing ◽  
Electric Power System ◽  
Percentage Error ◽  
Gradual Change ◽  
Artificial Neural

An accurate demand forecasting is essential for planning the electric dispatch in power system, contributing financially to electricity companies and helping in the security and continuity of electricity supply. In addition, it is evident that the distributed energy resource integration in the electric power system has been increasing recently, mostly from the photovoltaic generation, resulting in a gradual change of the load curve profile. Therefore, the 24 hours ahead prediction of the electrical demand of Campina Grande, Brazil, was realized from artificial neural network with a focus on the data preprocessing. Thus, the time series variations, such as hourly, diary and seasonal, were reduced in order to obtain a better demand prediction. Finally, it was compared the results between the forecasting with the preprocessing application and the prediction without the  preprocessing stage. Based on the results, the first methodology presented lower mean absolute percentage error with 7.95% against 10.33% of the second one.

scholarly journals Daily Peak Load Forecast Using Artificial Neural Network

2019 ◽  
Vol 9 (4) ◽  
pp. 2256
Author(s):  
Ramesh Kumar V ◽  
Pradipkumar Dixit
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Learning Algorithm ◽  
Peak Load ◽  
Load Forecasting ◽  
Percentage Error ◽  
Ann Model ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Artificial Neural

The paper presents an Artificial Neural Network (ANN) model for short-term load forecasting of daily peak load. A multi-layered feed forward neural network with Levenberg-Marquardt learning algorithm is used because of its good generalizing property and robustness in prediction. The input to the network is in terms of historical daily peak load data and corresponding daily peak temperature data. The network is trained to predict the load requirement ahead. The effectiveness of the proposed ANN approach to the short-term load forecasting problems is demonstrated by practical data from the Bangalore Electricity Supply Company Limited (BESCOM). The comparison between the proposed and the conventional methods is made in terms of percentage error and it is found that the proposed ANN model gives more accurate predictions with optimal number of neurons in the hidden layer.

scholarly journals Short Term Load Forecasting of Distribution Feeder Using Artificial Neural Network Technique

2021 ◽  
Vol 2 (1) ◽  
pp. 1-22
Author(s):  
Kumilachew Chane ◽  
◽  
Fsaha Mebrahtu Gebru ◽  
Baseem Khan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Load Forecasting ◽  
Electric Utility ◽  
Percentage Error ◽  
Electrical Load ◽  
Short Term ◽  
Load Demand ◽  
Short Term Load Forecasting ◽  
Artificial Neural

This paper explains the load forecasting technique for prediction of electrical load at Hawassa city. In a deregulated market it is much need for a generating company to know about the market load demand for generating near to accurate power. If the generation is not sufficient to fulfill the demand, there would be problem of irregular supply and in case of excess generation the generating company will have to bear the loss. Neural network techniques have been recently suggested for short-term load forecasting by a large number of researchers. Several models were developed and tested on the real load data of a Finnish electric utility at Hawassa city. The authors carried out short-term load forecasting for Hawassa city using ANN (Artificial Neural Network) technique ANN was implemented on MATLAB and ETAP. Hourly load means the hourly power consumption in Hawassa city. Error was calculated as MAPE (Mean Absolute Percentage Error) and with error of about 1.5296% this paper was successfully carried out. This paper can be implemented by any intensive power consuming town for predicting the future load and would prove to be very useful tool while sanctioning the load.

scholarly journals Predicting Pressure Sensitivity to Luminophore Content and Paint Thickness of Pressure-Sensitive Paint Using Artificial Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5188
Author(s):  
Mitsugu Hasegawa ◽  
Daiki Kurihara ◽  
Yasuhiro Egami ◽  
Hirotaka Sakaue ◽  
Aleksandar Jemcov
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Data Augmentation ◽  
Percentage Error ◽  
Pressure Sensitivity ◽  
Pressure Sensitive Paint ◽  
Pressure Sensitive ◽  
Artificial Neural ◽  
Paint Thickness ◽  
Artificial Neural Network Ann

An artificial neural network (ANN) was constructed and trained for predicting pressure sensitivity using an experimental dataset consisting of luminophore content and paint thickness as chemical and physical inputs. A data augmentation technique was used to increase the number of data points based on the limited experimental observations. The prediction accuracy of the trained ANN was evaluated by using a metric, mean absolute percentage error. The ANN predicted pressure sensitivity to luminophore content and to paint thickness, within confidence intervals based on experimental errors. The present approach of applying ANN and the data augmentation has the potential to predict pressure-sensitive paint (PSP) characterizations that improve the performance of PSP for global surface pressure measurements.

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