Search results for "Load forecasting"

showing 3 items of 3 documents

Hybrid Particle Swarm Optimization With Genetic Algorithm to Train Artificial Neural Networks for Short-Term Load Forecasting

2019

This research proposes a new training algorithm for artificial neural networks (ANNs) to improve the short-term load forecasting (STLF) performance. The proposed algorithm overcomes the so-called training issue in ANNs, where it traps in local minima, by applying genetic algorithm operations in particle swarm optimization when it converges to local minima. The training ability of the hybridized training algorithm is evaluated using load data gathered by Electricity Generating Authority of Thailand. The ANN is trained using the new training algorithm with one-year data to forecast equal 48 periods of each day in 2013. During the testing phase, a mean absolute percentage error (MAPE) is used …

Artificial neural networkComputer sciencebusiness.industry020209 energyLoad forecastingTraining (meteorology)Particle swarm optimization02 engineering and technologyBackpropagationComputer Science ApplicationsTerm (time)Computational Theory and MathematicsArtificial IntelligenceGenetic algorithm0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingArtificial intelligencebusinessInternational Journal of Swarm Intelligence Research
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Daily Peak Temperature Forecasting with Elman Neural Networks

2005

This work presents a forecaster based on an Elman artificial neural network trained with resilient backpropagation algorithm for predicting the daily peak temperatures one day ahead. The available time series was recorded at Petrosino (TP), in the west coast of Sicily, Italy and it is composed by temperature (min and max values), the humidity (min and max values) and the rainfall value between January 1st, 1995 and May 14th, 2003. Performances and reliabilities of the proposed model were evaluated by a number of measures, comparing different neural models. Experimental results show very good prediction performances.

Artificial neural networkComputer sciencebusiness.industryLoad forecastingWeather forecastingHumiditycomputer.software_genreRpropBackpropagationStatisticsartificial neural networkTemperature forecastingPrecipitationWest coastArtificial intelligencebusinesscomputer
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External parameters contribution in domestic load forecasting using neural network

2015

Domestic demand prediction is very important for home energy management system and also for peak reduction in the power system network. In this work, for precise prediction of power demand, external parameters, such as temperature and solar radiation, are considered and included in the prediction model for improving prediction performance. Power prediction models for coming hours' power demand estimation are built using neural network based on hourly power consumptions data with / without ambient temperature data and global solar irradiation (GSI) data respectively. In this work, a typical Southern Norwegian household demand has been considered. As a result, both ambient temperature and GSI…

Energy management systemReduction (complexity)Electric power systemEngineeringWork (thermodynamics)Artificial neural networkbusiness.industryLoad forecastingbusinessPredictive modellingSimulationAutomotive engineeringPower (physics)International Conference on Renewable Power Generation (RPG 2015)
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