Application of artificial neural networks for testing long-term energy policy targets

Đozić, Damir J.; Urošević, Branka D. Gvozdenac

doi:10.1016/j.energy.2019.02.191

Cited by 33 publications

(20 citation statements)

References 21 publications

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“…26 Kong et al 27 has introduced the concept of deep learning to detect network disruption, a combination of wavelet conversion, multiresolution single universe, and deeplearning geometry. It is classified into supervised, semisupervised, or unsupervised.…”

Section: Introductionmentioning

confidence: 99%

“…It is classified into supervised, semisupervised, or unsupervised. 26 Kong et al 27 has introduced the concept of deep learning to detect network disruption, a combination of wavelet conversion, multiresolution single universe, and deeplearning geometry. The simulation results in this study proved that the use of this concept can achieve the desired goal and is very accurate.…”

Section: Introductionmentioning

confidence: 99%

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Experimental and deep learning artificial neural network approach for evaluating grid-connected photovoltaic systems

et al. 2019

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Summary This article evaluates a 1.4‐kW building integrated grid‐connected photovoltaic plant. The PV plant was installed in the Faculty of Engineering solar energy lab, Sohar University, Oman, and the system data have been collected for a year from July 2017 to June 2018. The grid‐connected system was evaluated in terms of power, energy, specific yield, capacity factor, and cost of energy, and payback period. The measured diffuse and global solar irradiations are 3289 and 6182 Wh/m2, respectively. Four predictive models (TLRN, FRNN‐1, FRNN‐2, and FRNN‐3) using deep learning approach based on RNN and TLRN were proposed to predict the PV current performance through data input of temperature (T) and solar irradiance (G). The experiment results found that the highest energy production, array, reference, and final yields are 245.8 kWh, 3.43 to 5.65 kWh/kWp‐day, 4.61 to 7.33 kWh/kWp‐day, and 3.24 to 4.82 kWh/kWp‐day, respectively. Meanwhile, CF, CoE, and PBP were found to be 21.7%, 0.045 USD/kWh and 11.17 years, respectively. The highest performance for prediction models were found for FRNN‐2 and FRNN‐3 due to they exhibit lower MSE which means being tightly fitted to experiments.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Experimental and deep learning artificial neural network approach for evaluating grid-connected photovoltaic systems

et al. 2019

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“…For the aim of finding the optimum ANN structure, several parameters should be adjusted. The most influential parameters in ANN are the number of hidden layers, the number of neurons in each hidden layer, and the transfer function [66]. A trial-and-error method is implemented to determine the optimum values of the aforementioned parameters with the objective of minimum error.…”

Section: Ann Architecturementioning

confidence: 99%

Forecasting Wastewater Temperature Based on Artificial Neural Network (ANN) Technique and Monte Carlo Sensitivity Analysis

2020

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Wastewater contains considerable amounts of thermal energy. Heat recovery from wastewater in buildings could supply cities with an additional source of renewable energy. However, variations in wastewater temperature influence the performance of the wastewater treatment plant. Thus, the treatment is negatively affected by heat recovery upstream of the plant. Therefore, it is necessary to develop more accurate models of the wastewater temperature variations. In this work, a computational model based on artificial neural network (ANN) is proposed to calculate wastewater treatment plant influent temperature concerning ambient temperature, building effluent temperature and flowrate, stormwater flowrate, infiltration flowrate, the hour of day, and the day of year. Historical data related to the Stockholm wastewater system are implemented in MATLAB software to drive the model. The comparison of calculated and observed data indicated a negligible error. The main advantage of this ANN model is that it only uses historical data commonly recorded, without any requirements of field measurements for intricate heat transfer models. Moreover, Monte Carlo sensitivity analysis determined the most influential parameters during different seasons of the year. Finally, it was shown that installing heat exchangers in 40% of buildings would reduce 203 GWh year−1 heat loss in the sewage network. However, heat demand in WWTP would be increased by 0.71 GWh year−1, and the district heating company would recover 176 GWh year−1 less heat from treated water.

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“…The economic analysis aims to study the feasibility of strategy adjustment and upgrade in relevant energy industries, involving many fields, e.g., coal-fired efficiency [38], heat recovery and storage [39], battery storage [40], CO 2 capture [41], electricity supply with renewable energy promotion [42], drying process [43], HVAC (heating ventilation and air conditioning) [44], etc. Meanwhile, the process of energy production [45] and transmission [46] as well as the negative effects on the environment such as pollutant emission [47] and the greenhouse effect [48] should be considered.…”

Section: Energy Policy For Sustainable Developmentmentioning

confidence: 99%

Recent Advances in Technology, Strategy and Application of Sustainable Energy Systems

et al. 2020

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The global COVID-19 pandemic has had strong impacts on national and international freight, construction and tourism industry, supply chains, and has resulted in a rapid decline in the demand for traditional energy sources. In fact, research has outlined that urban areas depend on global supply chains for their day-to-day basic functions, including energy supplies, food and safe access to potable water. The disruption of global supply chains can leave many urban areas in a very vulnerable position, in which their citizens may struggle to obtain their basic supplies, as the COVID-19 crisis has recently shown. Therefore, solutions aiming to enhance local food, water and energy production systems, even in urban environments, have to be pursued. The COVID-19 crisis has also highlighted in the scientific community the problem of people’s exposure to outdoor and indoor pollution, confirmed as a key element for the increase both in the transmission and severity of the contagion, on top of involving health risks on their own. In this context, most nations are going to adopt new preferential policies to stimulate the development of relevant sustainable energy industries, based on the electrification of the systems supplied by renewable energy sources as confirmed by the International Energy Agency (IEA). Thus, while there is ongoing research focusing on a COVID 19 vaccine, there is also a need for researchers to work cooperatively on novel strategies for world economic recovery incorporating renewable energy policy, technology and management. In this framework, the Sustainable Development of Energy, Water and Environment Systems (SDEWES) conference provides a good platform for researchers and other experts to exchange their academic thoughts, promoting the development and improvements on the renewable energy technologies as well as their role in systems and in the transition towards sustainable energy systems. The 14th SDEWES Conference was held in Dubrovnik, Croatia. It brought together around 570 researchers from 55 countries in the field of sustainable development. The present Special Issue of Energies, specifically dedicated to the 14th SDEWES Conference, focuses on four main fields: energy policy for sustainable development, biomass energy application, building energy saving, and power plant and electric systems.

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Application of artificial neural networks for testing long-term energy policy targets

Cited by 33 publications

References 21 publications

Experimental and deep learning artificial neural network approach for evaluating grid-connected photovoltaic systems

Experimental and deep learning artificial neural network approach for evaluating grid-connected photovoltaic systems

Forecasting Wastewater Temperature Based on Artificial Neural Network (ANN) Technique and Monte Carlo Sensitivity Analysis

Recent Advances in Technology, Strategy and Application of Sustainable Energy Systems

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