Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand

Anand, Atul; Suganthi, L

doi:10.20944/preprints201801.0216.v1

Cited by 10 publications

(8 citation statements)

References 2 publications

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“…By combining this two methods, an optimized result can be achieved. (Anand and Suganthi 2018) use an ANN, which is optimized by a hybrid algorithm of GA and PSO.…”

Section: Hybrid Methodsmentioning

confidence: 99%

Demand Prediction with Machine Learning Models; State of the Art and a Systematic Review of Advances

Mosavi¹,

Ardabili²,

Shamshirband³

2019

Preprint

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Electricity demand prediction is vital for energy production management and proper exploitation of the present resources. Recently, several novel machine learning (ML) models have been employed for electricity demand prediction to estimate the future prospects of the energy requirements. The main objective of this study is to review the various ML models applied for electricity demand prediction. Through a novel search and taxonomy, the most relevant original research articles in the field are identified and further classified according to the ML modeling technique, perdition type, and the application area. A comprehensive review of the literature identifies the major ML models, their applications and a discussion on the evaluation of their performance. This paper further makes a discussion on the trend and the performance of the ML models. As the result, this research reports an outstanding rise in the accuracy, robustness, precision and the generalization ability of the prediction models using the hybrid and ensemble ML algorithms.

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“…By combining this two methods, an optimized result can be achieved. (Anand and Suganthi 2018) use an ANN, which is optimized by a hybrid algorithm of GA and PSO.…”

Section: Hybrid Methodsmentioning

confidence: 99%

Demand Prediction with Machine Learning Models; State of the Art and a Systematic Review of Advances

Mosavi¹,

Ardabili²,

Shamshirband³

2019

Preprint

View full text Add to dashboard Cite

show abstract

“…FSA incorporate to solutions in wireless sensor networks [30,32,33], tracking [34], medical estimations [35][36][37], segmentation [32], clustering [33], regression [38], image processing [39,40], calibration [35], localization [41], power systems [36,42].…”

Section: Applicationsmentioning

confidence: 99%

Swarm Intelligence - Recent Advances and Current Applications

2023

Artificial Intelligence

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Since the earliest humans to populate the earth, we have gradually tried to understand and control the world around us. In trying to understand various phenomena, humans began to make predictions, for example, about the motions of the planets, eclipses, cycles of rainfall, or the periodicity of certain diseases. However, in the last few decades, the complexity of the predictions needed to be carried out has reached the limits of human ability. Luckily, the dawn of electronic computers has increased our abilities to predict nature, although the problems we are facing now are far more complex than the problems we faced a century ago.Artificial intelligence (AI) machines demonstrate advanced cognitive skills in taking decisions, learning, perceiving the environment, predicting certain behavior, and processing written or spoken languages, among other skills. In addition, the ability of certain species to collectively demonstrate "swarm intelligence" has proven to be useful for solving complex problems that otherwise would be difficult to tackle. The collective power of AI and swarm intelligence can help solve difficult and complicated problems in innumerable areas.This book highlights the wide range of applications in swarm intelligence and AI. It is divided into two sections: "Theoretical Background" and "Trends and Applications". It is a useful resource for researchers and students alike.

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“…Therefore, we will save the first three best solutions and force other search agents (omegas) to update their position according to the position of the best search agents based on Eqs. (10)(11)(12)(13)(14)(15)(16).…”

Section: Detecting the Position Of The Preymentioning

confidence: 99%

“…Evaluations carried out by the error index indicate that the use of the genetic algorithm improves prediction performance up to 1.5%. In 2018, Anand and suganthi [11] proposed an ANN that is optimized with hybrid genetic algorithm and particle swarm optimization (GA-PSO) to predict the electricity demand of the state of Tamil Nadu in India. The obtained results showed that the proposed method have higher accuracy than single optimization models such as PSO and GA.…”

Section: Introductionmentioning

confidence: 99%

An Improved Artificial Intelligence Based on Gray Wolf Optimization and Cultural Algorithm to Predict Demand for Dairy Products: A Case Study

Goli¹,

Zare²,

Tavakkoli–Moghaddam³

et al. 2019

IJIMAI

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This paper provides an integrated framework based on statistical tests, time series neural network and improved multi-layer perceptron neural network (MLP) with novel meta-heuristic algorithms in order to obtain best prediction of dairy product demand (DPD) in Iran. At first, a series of economic and social indicators that seemed to be effective in the demand for dairy products is identified. Then, the ineffective indices are eliminated by using Pearson correlation coefficient, and statistically significant variables are determined. Then, MLP is improved with the help of novel meta-heuristic algorithms such as gray wolf optimization and cultural algorithm. The designed hybrid method is used to predict the DPD in Iran by using data from 2013 to 2017. The results show that the MLP offers 71.9% of the coefficient of determination, which is better compared to the other two methods if no improvement is achieved.

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Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand

Cited by 10 publications

References 2 publications

Demand Prediction with Machine Learning Models; State of the Art and a Systematic Review of Advances

Demand Prediction with Machine Learning Models; State of the Art and a Systematic Review of Advances

Swarm Intelligence - Recent Advances and Current Applications

An Improved Artificial Intelligence Based on Gray Wolf Optimization and Cultural Algorithm to Predict Demand for Dairy Products: A Case Study

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