Appraisal of artificial neural network for forecasting of economic parameters

Kordanuli, Bojana; Barjaktarović, Lidija; Jeremić, Ljiljana; Alizamir, Meysam

doi:10.1016/j.physa.2016.08.062

Cited by 35 publications

(13 citation statements)

References 16 publications

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“…The input and output layer's weights and biases are mutually optimised until the output neuron values are within possibility of weeding out false reasoning errors. This approach was successfully applied in response to regression problems [45]. This feed-forward network model is presented in Figures 2 and 3.…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Short-Term Power Prediction of Building Integrated Photovoltaic (BIPV) System Based on Machine Learning Algorithms

Kabilan¹,

Venkatesan

Yogapriya³

et al. 2021

International Journal of Photoenergy

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One of the biggest challenges is towards ensuring large-scale integration of photovoltaic systems into buildings. This work is aimed at presenting a building integrated photovoltaic system power prediction concerning the building’s various orientations based on the machine learning data science tools. The proposed prediction methodology comprises a data quality stage, machine learning algorithm, weather clustering assessment, and an accuracy assessment. The results showed that the application of linear regression coefficients to the forecast outputs of the developed photovoltaic power generation neural network improved the PV power generation’s forecast output. The final model resulted from accurate forecasts, exhibiting a root mean square error of 4.42% in NN, 16.86% in QSVM, and 8.76% in TREE. The results are presented with the building facade and roof application such as flat roof, south façade, east façade, and west façade.

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Section: Machine Learning Algorithmsmentioning

confidence: 99%

Short-Term Power Prediction of Building Integrated Photovoltaic (BIPV) System Based on Machine Learning Algorithms

Kabilan¹,

Venkatesan

Yogapriya³

et al. 2021

International Journal of Photoenergy

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“…BPNN is suitable for solving the problems that have a lot of input and output data but cannot fi nd the relationship between input and output, have high complexity, the solution to problem keep changing overtime and output can be fuzzy. Consequently, BPNN has been successfully applied to various areas such as chemical [5], transportation [6], ergonomics [7], banking [8], marketing [9], economics [10][11], medical [12][13], and energy [14][15] and others. Later, BPNN was an algorithm that used for training feedforward network inspired [16].…”

Section: Backpropagation Neural Network (Bpnn)mentioning

confidence: 99%

Thailand port throughput prediction via particle swarm optimization based neural network

Kunnapapdeelert¹,

Thepmongkorn²

2020

J Appl Eng Science

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Shipping volume in Thailand have signifi cantly increased in last four years. It is important to pay attention to the trend of Thailand port throughput and use as the guideline to prepare for the needs of supporting facilities, infrastructures, fi nancial and human resources. An effective forecasting technique called particle swarm based neural network (PSO-NN) is developed to estimate Thailand port throughput in this work. The prediction results from PSONN and classical backpropagation training algorithm, backpropagation neural networks (BPNN) were compared. The results shown that PSONN provides more accurate results than BPNN when apply to predict port throughput of Thailand. The mean squared error obtained from PSONN are about 10 times lower than that of BPNN. This confi rms that neural network based on PSO training algorithm has better performance and better ability to escape local optimum than that of BPNN.

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“…The capabilities of the NN and application in complex and nonlinear problems, make this method preferable to the regression method and many researchers have used NN in literatures. In summary, the NN method has been used in management (Kasiviswanathan et al, 2016), economics (Kordanuli et al, 2017), manufacturing (Conde et al, 2018), agriculture (Espinoza et al, 2016), banking (Kwon and Lee, 2015), pharmacy (Vasilakos et al, 2016), energy (Zeng et al, 2017, efficiency evaluations (Emrouznejad and Shale, 2009) and etc.…”

Section: Literature Reviewmentioning

confidence: 99%

Finding the optimal combination of power plants alternatives: A multi response Taguchi-neural network using TOPSIS and fuzzy best-worst method

Omrani

Alizadeh

Emrouznejad

2018

Journal of Cleaner Production

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With increasing growth of electricity consumption in developed and developing countries, the necessity of constructing and developing of power plants is inevitable. There are two main resources for electricity generation includes fossil and renewable energies which have some different characteristics such as manufacturing technology, environmental issues, accessibility and etc. In developing plans, it is important to consider and address the policy makers' indicators such as environmental, social, economic and technical criteria. In this paper, an integrated multi response Taguchi-neural network-fuzzy best-worst method (FBWM)-TOPSIS approach is applied to find an optimal level of five different power plants including: gas, steam, combined cycle, wind and hydroelectric. Taguchi method is used to design combinations and calculate some of the signal to noise (S/N) ratios. Then, neural network is applied to estimate the rest of S/N ratios. Finally, FBWM and TOPSIS methods are used for weighing subindicators and selecting the best combination, respectively. To illustrate the usefulness of the proposed approach, a case study on the development of power plants in Iran is considered and the results are discussed. According to the results, in general, small size power plants for fossil resources are preferable. In contrast, medium and larger size power plants for renewable resources are preferable.

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Appraisal of artificial neural network for forecasting of economic parameters

Cited by 35 publications

References 16 publications

Short-Term Power Prediction of Building Integrated Photovoltaic (BIPV) System Based on Machine Learning Algorithms

Short-Term Power Prediction of Building Integrated Photovoltaic (BIPV) System Based on Machine Learning Algorithms

Thailand port throughput prediction via particle swarm optimization based neural network

Finding the optimal combination of power plants alternatives: A multi response Taguchi-neural network using TOPSIS and fuzzy best-worst method

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