Modeling the Levelized Cost of Energy for Concentrating Solar Thermal Power Systems Based on a Nonlinear AutoRegressive Neural Network With Exogenous Inputs

This article presents the results of the development of a mathematical model for predicting the levelized cost of energy (LCOE) for solar concentrating thermal power systems (CSP systems) based on a nonlinear autoregressive neural network with exogenous inputs (NARX). A two-layer NARX network with sigmoid hidden neurons and linear output neurons has been developed. The input layer is made up of the following variables: the volume of input power of CSP systems in the world, the total world energy consumption, domestic energy consumption, domestic gas consumption, domestic consumption of coal and lignite, domestic energy consumption, the share of renewable energy in electricity generation, the share of wind and solar energy in the production of electricity, carbon dioxide emissions from fuel combustion, the price of Brent oil against the US dollar, and the average price for natural gas auctions. The output layer specifies LCOE values for CSP systems.

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Prediction of Levelized Cost of Electricity for Solar Photovoltaic Systems by Using Neural Networks

Vestnik MEI ◽

10.24160/1993-6982-2021-4-53-58 ◽

2021 ◽

pp. 53-58

Author(s):

Natalya S. Filippchenkova ◽

Keyword(s):

Neural Network ◽

Carbon Dioxide Emissions ◽

Economic Indicators ◽

Photovoltaic Systems ◽

Special Importance ◽

Solar Photovoltaic ◽

Levelized Cost Of Electricity ◽

Levelized Cost ◽

Marquardt Algorithm ◽

Nonlinear Autoregressive

Elaboration of a new approach to the development of models for predicting the economic indicators of solar photovoltaic systems by using artificial neural network algorithms is becoming of special importance. As is known, the relationships between economic indicators are often difficult to identify. Nonlinear autoregressive models can provide more reliable results than those obtained from predictive linear models based on vector autoregression. The article presents the results from the development of a mathematical model for predicting the levelized cost of energy (LCOE) for solar photovoltaic systems based on a nonlinear autoregressive neural network with exogenous inputs (NARX). A two-layer NARX network with hidden sigmoid neurons and linear output neurons has been developed. The input layer is made up of the following variables: the amount of power consumed from solar photovoltaic systems around the world; the total worldwide energy consumption; domestic consumption of energy, gas, coal, and lignite; the shares of renewable energy, wind and solar energy in electricity generation; carbon dioxide emissions from fuel combustion; the price of Brent oil in US dollars, and the average price for natural gas. The output layer determines the LCOE values for solar photovoltaic systems. The developed NARX network was trained on the basis of retrospective data for 2005-2010 using the Levenberg-Marquardt algorithm. The correlation coefficient value achieved in the course of training made 0.99904, and the mean square error value was in the range from 0.00042 to 0.0029

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Prediction of energy consumption using recurrent neural networks (RNN) and nonlinear autoregressive neural network with external input (NARX)

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v17.i3.pp1215-1223 ◽

2020 ◽

Vol 17 (3) ◽

pp. 1215

Author(s):

Wan Muhammad Zafri Wan Yahaya ◽

Fadhlan Hafizhelmi Kamaru Zaman ◽

Mohd Fuad Abdul Latip

Keyword(s):

Neural Network ◽

Neural Networks ◽

Energy Consumption ◽

Recurrent Neural Networks ◽

Electrical Energy ◽

External Input ◽

Smart Meters ◽

Depth Analysis ◽

Nonlinear Autoregressive ◽

Autoregressive Neural Network

Recurrent Neural Networks (RNN) and Nonlinear Autoregressive Neural Network with External Input (NARX) are recently applied in predicting energy consumption. Energy consumption prediction for depth analysis of how electrical energy consumption is managed on Tower 2 Engineering Building is critical in order to reduce the energy usage and the operational cost. Prediction of energy consumption in this building will bring great benefits to the Faculty of Electrical Engineering UiTM Shah Alam. In this work, we present the comparative study on the performance of prediction of energy consumption in Tower 2 Engineering Building using RNN and NARX method. The model of RNN and NARX are trained using data collected using smart meters installed inside the building. The results after training and testing using RNN and NARX show that by using the recorded data we can accurately predict the energy consumption in the building. We also show that RNN model trained with normalized data performs better than NARX model.

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SOH estimation method for lithium-ion batteries based on nonlinear autoregressive neural network model with exogenous input

Journal of Electrochemical Energy Conversion and Storage ◽

10.1115/1.4052274 ◽

2021 ◽

pp. 1-28

Author(s):

Yanbo Che ◽

Yibin Cai ◽

Hongfeng Li ◽

Yushu Liu ◽

Mingda Jiang ◽

...

Keyword(s):

Neural Network ◽

Lithium Ion Batteries ◽

Equivalent Circuit ◽

Equivalent Circuit Model ◽

Lithium Ion ◽

Circuit Model ◽

Variable Order ◽

Nonlinear Correlation ◽

Nonlinear Autoregressive ◽

Autoregressive Neural Network

Abstract The working state of lithium-ion batteries must be estimated accurately and efficiently in the battery management system. Building a model is the most prevalent way of predicting the battery's working state. Based on the variable order equivalent circuit model, this paper examines the attenuation curve of battery capacity with the number of cycles. It identifies the order of the equivalent circuit model using Bayesian Information Criterion (BIC). Based on the correlation between capacity and resistance, the paper concludes that there is a nonlinear correlation between model parameters and state of health (SOH). The nonlinear autoregressive neural network with exogenous input (NARX) is used to fit the nonlinear correlation for capacity regeneration. Then, the self-adaptive weight particle swarm optimization (SWPSO) method is suggested to train the neural network. Finally, single-battery and multi-battery tests are planned to validate the accuracy of the SWPSO-NARX estimate of SOH. The experimental findings indicate that the SOH estimate effect is significant.

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