Showing papers in "Energy in 2020"

TL;DR: The obtained empirical results show that the proposed forecasting model can capture the nonlinear properties of crude oil time series, and that better forecasting performance can be obtained in terms of precision and volatility than the other current forecasting models.

TL;DR: In this paper, the effect of renewable energy consumption on economic growth across 38 renewable-energy consuming countries from 1990 to 2018 was analyzed using dynamic ordinary least squares (DOLS), fully modified ordinary least square (FMOLS) and heterogeneous non-causality approaches.

TL;DR: In this paper, the recent findings regarding the application of various pretreatment techniques such as chemical, physical and biological methods for bioethanol production from lignocellulosic biomass have been reviewed.

TL;DR: In this paper, the impact of urbanization and international trade on CO2 across 65 Belt and Road Initiative (BRI) countries from 2000 to 2016 was investigated through panel quantile regression.

TL;DR: In this paper, the options for carbon emission reductions are grouped into (1) generation of secondary energy carriers, (2) end-use energy sectors and (3) sector interdependencies.

TL;DR: Results show that the proposed Gaussian process regression model based on the partial incremental capacity curve can provide accurate and robust state of health estimation.

TL;DR: Techniques for efficient solar collection, thermal storage, and power generation at >700 °C and barriers on the way to the high-temperature CSP are summarized.

TL;DR: In this article, a detailed illustration of phase change materials and their working principle, different types, and properties are provided, and a characteristic example of PCM in solar energy storage and the design of PCMs are reviewed and analyzed.

TL;DR: A data-driven method based on Gaussian process regression (GPR) is proposed to provide a feasible solution to SOC estimation of battery packs, and its superiority includes the ability to approximate nonlinearity accurately, nonparametric modeling, and probabilistic predictions.

TL;DR: Wang et al. as discussed by the authors used the difference-in-differences (DID) method to evaluate the impact of carbon emissions and economic growth following carbon emission trading implementation in China.

TL;DR: The main observation pinpointed is that with a proper design standard, material selection and user guideline, reusable PPE could be an effective option with lower energy consumption/environmental footprint and protecting efficiency returned on environmental footprint invested for masks.

TL;DR: In this article, the authors employed STIRPAT (stochastic impact of regression on population, affluence, and technology) model to investigate the impact of natural resource depletion on energy use and carbon dioxide emissions for a panel of 56 BRI countries over 1990-2014.

TL;DR: A detailed review of essential process parameters and identifies gaps and solutions for effective implementation of the anaerobic digestion of organic fraction of municipal solid waste is presented in this paper. But, the data suggests that it is not still widely applied for energy recovery from organic wastes at centralised level.

TL;DR: A long short-term memory – recurrent neural network is proposed to model the sophisticated battery behaviors under varying temperatures and estimate battery SOC from voltage, current, and temperature variables and provides a satisfying SOC estimation under other temperatures which have no data trained before.

TL;DR: It is observed that EHHO can be used as an effective method for parameter estimation of solar cells and photovoltaic modules, and accuracy, reliability, and other aspects of this method are better than most existing methods.

TL;DR: Considering the limitation of missing certain measuring equipments, new prediction models with the reduced secondary variables are retrained to explore the relationship between the prediction accuracy and the potential input variables, and demonstrate that the proposed algorithm has the excellent generalization capability.

TL;DR: An improved energy management framework that embeds expert knowledge into deep deterministic policy gradient (DDPG) is proposed and shows that the proposed EMS outperforms the one without prior knowledge and the other state-of-art deep reinforcement learning approaches.

TL;DR: In this article, the nonlinear relationship between renewable energy and economic growth in OECD countries was investigated by developing panel threshold regression models, and three panel threshold models were developed based on these three threshold variables to explore the internal mechanism of renewable energy for economic development.

TL;DR: This paper proposes a novel sequence-to-sequence model using the Attention-based Gated Recurrent Unit (AGRU) that improves accuracy of forecasting processes and embeds the task of correlating different forecasting steps by hidden activations of GRU blocks.

TL;DR: In this article, a comprehensive review of recent literature and projects is presented, with particular attention on RAs' roles in electricity markets as well as their difference from other market entities, and the business model for RA is analyzed systematically, involving resource aggregation, basic information prediction, market bidding strategy development, and settlement process.

TL;DR: In this paper, a case study of a village in West China by performing simulation, optimization, and sensitivity analysis is presented, where different combinations of PV panels, wind turbine and biogas generator are modeled and optimized in Hybrid Optimization Model for Electric Renewables.

TL;DR: In this article, a hybrid photovoltaics/wind turbine/biogas generator/fuel cell renewable energy system is proposed and analyzed for both stand-alone and on-grid application.

TL;DR: A data-driven prediction technique, support vector regression for establishing a battery degradation model, which estimates battery capacity by partial incremental capacity curves, which is based on the support vectors regression algorithm.

TL;DR: In this paper, the feasibility of using cost-effective glycol-ZnO nanofluid in spectral splitting concentrating photovoltaic thermal (CPV/T) system was experimentally verified.

TL;DR: Simulation results demonstrate that the hybrid algorithm obtains results that are more accurate than other applied algorithms, and show that the use of solar energy with a diesel generator, compared to the diesel only system, significantly reduces greenhouse gas emissions and supply costs.

TL;DR: The Independence Performance Index (IPI) is introduced for the MGs to reduce energy exchange with the main grid and improve system losses, voltage drop, and greenhouse gas emissions.

TL;DR: A crossover experiment with 240 schemes of WT parameter selection is designed and the proposed model outperforms other AI models, such as back propagation neural network et al., in forecasting accuracy and provides an effective reference for the application of WT in other forecasting scenarios and for electricity market participants.

TL;DR: A new method that is combining the computation and Harris Hawk Optimization (HHO) algorithm to extract the unknown parameters of the TDPV model is presented, which can be easily applied to identify the electrical parameters of any commercial PV panel based on the datasheet values only.

TL;DR: In this article, three different configurations of a hybrid battery thermal management system (BTMS) using phase change material (PCM) as passive and air coolant as active cooling system were investigated.

TL;DR: In this article, the primary economic and political factors shaping transitions to a low carbon economy via renewable energy generation in post-socialist countries were analyzed using extensive data from 27 transition economies over the years 1990-2014.