• Corpus ID: 14357741

Electrical Load Forecasting in Power Distribution Network by Using Artificial Neural Network

@inproceedings{Nahari2013ElectricalLF,
  title={Electrical Load Forecasting in Power Distribution Network by Using Artificial Neural Network},
  author={Ali Nahari and Habib Rostami and Rahman Dashti},
  year={2013}
}
Today, one of most important concerns in electrical power markets and distribution network is supplying the customer demands. In order to manage the market it is necessary to forecast the usage of electrical power in distribution network. The pattern of electrical power usage depends on many different parameters such as the week days, seasons, weather condition and etc. Today, researchers by using an artificial intelligence based on the natural intelligence are trying to forecast the costumers… 

Figures and Tables from this paper

Short-Term Forecasting of Electricity Consumption in Palestine Using Artificial Neural Networks
TLDR
A model is proposed that is used to predict the future electricity consumption depending on the previous consumption, which provides companies and authorities to know the future information about the electricity consumption, so they can organize their distribution and make suitable plans to maintain the stability in the delivery and distribution of electricity.
Monthly Maximum load Demand Forecasting for Sulaimani Governorate Using Different Weather Conditions Based on Artificial Neural Network Model
TLDR
This paper presents a monthly peak load demand forecasting for Sulaimani 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.
A Short-Term Load Forecasting of 33kV, 11kV and 415V Electrical Systems using HMLP Network
TLDR
It showed that Adaptive Learning Recursive Prediction Error learning algorithm can be more enhanced the performance of other learning algorithm as an online model in the series of 0.45 dB to 9.481 dB of Mean Square Error during validation.
Long-Term Load Forecasting of Southern Governorates of Jordan Distribution Electric System
Load forecasting is vitally important for electric industry in the deregulated economy. This paper aims to face the power crisis and to achieve energy security in Jordan. Our participation is
Spatial Load Forecast and estimation of the peak electricity demand location for Tafila region
TLDR
The Spatial Load Forecasting model used, the information provided by electrical distribution company in Jordan, the workflow followed, the parameters used and the assumptions made to run the model are described.
A Parameter-Free Approach for Fault Section Detection on Distribution Networks Employing Gated Recurrent Unit
Faults in distribution networks can result in severe transients, equipment failure, and power outages. The quick and accurate detection of the faulty section enables the operator to avoid prolonged

References

SHOWING 1-10 OF 10 REFERENCES
Comparison of Conventional and Modern Load Forecasting Techniques Based on Artificial Intelligence and Expert Systems
This paper picturesquely depicts the comparison of different methodologies adopted for predicting the load demand and highlights the changing trend and values under new circumstances using latest non
Different Methods of Long-Term Electric Load Demand Forecasting a Comprehensive Review
TLDR
An overview of the past and current practice in long- term demand forecasting is presented, which consists of some traditional methods, neural networks, genetic algorithms, fuzzy rules, support vector machines, wavelet networks and expert systems.
Short Term Load Forecasting using Generalized Neuron Model with Error Gradient Functions
TLDR
Development of STLF using GNM under different error gradients functions is obtained and generalized neuron model (GNM) has more flexibility, no hidden layers, less computation time, usage of  and  neurons etc.
Technical and economic analysis of different micropowers in providing network load and optimal selection with real load analysis of a 20KV/400V station in Bushehr Province of Iran
  • Ali Nahari, R. Dashti
  • Engineering
    2011 International Conference on Advanced Power System Automation and Protection
  • 2011
Increasing of the price of the fossil fuels and reducing them and their pollution, new clear energy have been more popular in the world but lack of notice to study of economic efficiently on new
Information System for Forecasting Processes Based on Unsupervised , Supervised Neural Networks
TLDR
An Information system for forecasting processes based on unsupervised, supervised neural networks is developed and the computation time for the neural network learning with KNN can largely reduced.
Short-term load forecasting using an artificial neural network
TLDR
A two-step training method to cope with a shortage of training data and overfitting problems is proposed and demonstrates improved accuracy over conventional methods, including ANNs which employ ordinary training algorithms.
Enhancement the accuracy of daily and hourly Short Time Load Forecasting using Neural Network
In this paper neural network has been attended for short-time load-forecasting, and forecasted results have tried to be more exact by suitable selecting of effectual factors on load forecasting. It
ISSN (Online): 2249–071X, ISSN (Print)
  • ISSN (Online): 2249–071X, ISSN (Print)
KarimiMadahi, “Enhancement the accuracy of daily and hourly Short Time Load Forecasting using Neural Network
  • Journal of Basic and Applied Scientific Research,
  • 2012