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

Anand, Atul; Suganthi, L

doi:10.20944/preprints201711.0190.v1

Cited by 2 publications

(3 citation statements)

References 12 publications

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“…Similarly, from Figure 8 , it can be seen that among the records of documents that reach HAP, the average MAPE value is lower in the frameworks that implement hybrid models of ML and multivariate dependency, such as those developed in [ 6 , 27 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 ]. To verify the hypotheses of the differences in the means and variances in the MAPE, three hypothesis tests are carried out.…”

Section: Evaluation Of Model Accuracymentioning

confidence: 85%

“…The ANN models have been used in many studies for electric power forecasting [ 6 , 39 , 44 , 46 , 48 , 53 , 54 , 55 , 57 , 58 , 59 , 60 , 61 , 62 , 65 , 70 , 75 , 77 , 81 , 82 , 86 , 108 , 153 , 155 , 165 , 175 , 176 ] and have reached a forcasting accuracy with an average MAPE value of 3.781%.…”

Section: Classes Of Forecasting Modelsmentioning

confidence: 99%

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A Systematic Review of Statistical and Machine Learning Methods for Electrical Power Forecasting with Reported MAPE Score

Vivas

Allende‐Cid

Salas

2020

Entropy

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Electric power forecasting plays a substantial role in the administration and balance of current power systems. For this reason, accurate predictions of service demands are needed to develop better programming for the generation and distribution of power and to reduce the risk of vulnerabilities in the integration of an electric power system. For the purposes of the current study, a systematic literature review was applied to identify the type of model that has the highest propensity to show precision in the context of electric power forecasting. The state-of-the-art model in accurate electric power forecasting was determined from the results reported in 257 accuracy tests from five geographic regions. Two classes of forecasting models were compared: classical statistical or mathematical (MSC) and machine learning (ML) models. Furthermore, the use of hybrid models that have made significant contributions to electric power forecasting is identified, and a case of study is applied to demonstrate its good performance when compared with traditional models. Among our main findings, we conclude that forecasting errors are minimized by reducing the time horizon, that ML models that consider various sources of exogenous variability tend to have better forecast accuracy, and finally, that the accuracy of the forecasting models has significantly increased over the last five years.

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Section: Evaluation Of Model Accuracymentioning

confidence: 85%

Section: Classes Of Forecasting Modelsmentioning

confidence: 99%

A Systematic Review of Statistical and Machine Learning Methods for Electrical Power Forecasting with Reported MAPE Score

Vivas

Allende‐Cid

Salas

2020

Entropy

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“…Aouni et al (2017) noted a need to aggregate several choices simultaneously to take decisions where accounting objectives are conflicting. Anand and Suganthi (2018) used ANN with different optimisation methods which are also useful to forecast electricity demands. Habib et al (2018) showed that well-documented high-quality financial reports contribute to economic growth.…”

Section: Neural Networkmentioning

confidence: 99%

Forecasting audit opinion based on multilevel perceptron neural network model using one-goal particle swarm optimisation

Bahrami¹,

Rezazadeh

Sarraf

2020

IJMP

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Audit opinion is not necessarily absolute due to constraints inherent in any audit work, including sampling, different convincing degrees of audit evidence, accounting system and internal control characteristics, as well as inherent constraints of accounting measurement. The information contained in audit reports is used to forecast corporate bankruptcy; therefore, the auditor's opinion influences the views of users of financial statements. Financial status of a business can be assessed using financial ratios. The circumstances considered by an auditor in commenting on financial distress including financials (in particular, short-term liquidity, ability to repay and debt tolerability), and operations (profitability and ability to generate cash from operations). This study uses an integrated ANN-PSO to forecast audit opinion. The results show that ANN-PSO is more useful, because PSO algorithm can optimise hidden layers and neurons in ANN simultaneously. Finally, ANN-PSO outperforms ANN.

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

Cited by 2 publications

References 12 publications

A Systematic Review of Statistical and Machine Learning Methods for Electrical Power Forecasting with Reported MAPE Score

A Systematic Review of Statistical and Machine Learning Methods for Electrical Power Forecasting with Reported MAPE Score

Forecasting audit opinion based on multilevel perceptron neural network model using one-goal particle swarm optimisation

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