Daily peak load forecasting using ANN

Tasre, Mohan B.; Bedekar, Prashant P.; Ghate, Vilas N.

doi:10.1109/nuicone.2011.6153291

Cited by 17 publications

(10 citation statements)

References 10 publications

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“…Furthermore, multilayer perceptrons neural network is frequently reported for short term load forecast problem [45]. A multilayer neural network structure with one input, hidden and output is shown in Fig.…”

Section: Multi-layer Perceptrons Neural Networkmentioning

confidence: 99%

A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings

Raza

Khosravi

2015

Renewable and Sustainable Energy Reviews

793

286

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Section: Multi-layer Perceptrons Neural Networkmentioning

confidence: 99%

A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings

Raza

Khosravi

2015

Renewable and Sustainable Energy Reviews

793

286

View full text Add to dashboard Cite

“…1. The single layer network is not able to learn the complex relationship between input and output but multilayer network (MNN) have the ability to learn the complex relationship [10]. The proposed ANN based forecast model 8:20:1 neurons in input, hidden, and output layer respectively are shown in Fig.…”

Section: Neural Network Based Load Forecast Modelmentioning

confidence: 99%

“…The ANN provides much better results as compared to previously implemented techniques [2,8]. The neural network has ability to solve the complex relationship between input and output and decision making under uncertainty and prediction patterns [9][10]. A number of different methods are applied for short term load forecast such as multilayer feed forward neural network, radial basis function Manuscript…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of Neural Network Based Short Term Load Forecast Models for Anomalous Days Load Prediction

Raza¹,

Baharudin²,

Badar-Ul-Islam³

et al. 2014

JCP

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Load forecasting plays a very vital role for efficient and reliable operation of the power system. Often uncertainties significantly decrease the prediction accuracy of load forecasting which affect the operational cost dramatically. In this paper, comparison of Back Propagation (BP) and Levenberg Marquardt (LM) neural network (NN) forecast model for 24 hours ahead is presented. The impact of lagged load data, calendar events and weather variables on load demand are analyzed in order to select the best forecast model inputs. The mean absolute percentage errors (MAPE), Daily peak error and regression analysis of NN training are used to measure the NN performance. The Forecast results demonstrate that, LM based forecast model outperform than BP NN model for performance matrices. This model is used to predict the load of ISO-New England grid.

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“…The future load is then predicted by variating weather information along with present historical data to present a relation among them [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Survey on short-term load forecasting using hybrid neural network techniques

Dalela¹,

Verma²,

Amutha³

2018

IJET

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Load forecasting is an issue of great importance for the reliable operation of the electric power system grids. Various forecasting methodologies have been proposed in the international research bibliography, following different models and mathematical approaches. In the current work, several latest methodologies based on artificial neural networks along with other techniques have be discussed, in order to obtain short-term load forecasting. In this paper, approaches taken by different researchers considering different parameters in means of predicting the lease error has been shown. The paper investigates the application of artificial neural networks (ANN) with fuzzy logic (FL), Genetic Algorithm(GA), Particle Swarm Optimization(PSO) and Support Vector Machines(SVM) as forecasting tools for predicting the load demand in short term category. The extracted outcomes indicate the effectiveness of the proposed method, reducing the relative error between real and theoretical data.

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Daily peak load forecasting using ANN

Cited by 17 publications

References 10 publications

A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings

A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings

A Comparative Analysis of Neural Network Based Short Term Load Forecast Models for Anomalous Days Load Prediction

Survey on short-term load forecasting using hybrid neural network techniques

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