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International Journal of Renewable Energy Development
Published by Center of Biomass and Renewable Energy
ISSN : 22524940     EISSN : 27164519     DOI : https://doi.org/10.61435/ijred.xxx.xxx
Core Subject : Science, Engineering,
Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering
The International Journal of Renewable Energy Development - (Int. J. Renew. Energy Dev.; p-ISSN: 2252-4940; e-ISSN:2716-4519) is an open access and peer-reviewed journal co-published by Center of Biomass and Renewable Energy (CBIORE) that aims to promote renewable energy researches and developments, and it provides a link between scientists, engineers, economist, societies and other practitioners. International Journal of Renewable Energy Development is currently being indexed in Scopus database and has a listing and ranking in the SJR (SCImago Journal and Country Rank), ESCI (Clarivate Analytics), CNKI Scholar as well as accredited in SINTA 1 (First grade category journal) by The Directorate General of Higher Education, The Ministry of Education, Culture, Research and Technology, The Republic of Indonesia under a decree No 200/M/KPT/2020. The scope of journal encompasses: Photovoltaic technology, Solar thermal applications, Biomass and Bioenergy, Wind energy technology, Material science and technology, Low energy architecture, Geothermal energy, Wave and tidal energy, Hydro power, Hydrogen production technology, Energy policy, Socio-economic on energy, Energy efficiency, planning and management, Life cycle assessment. The journal also welcomes papers on other related topics provided that such topics are within the context of the broader multi-disciplinary scope of developments of renewable energy.
Arjuna Subject : Energi (semua kategori) - Energi Terbarukan, Keberlanjutan dan Lingkungan
Articles 709 Documents
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New energy output prediction and demand response optimization based on LSTM-BN Wang, Weisheng; Shi, Wenhui; Nan, Dongliang; Peng, Yinzhang; Wang, Qinghua; Zhu, Yankai
International Journal of Renewable Energy Development Vol 14, No 1 (2025): January 2025
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2025.60632

Abstract

The study proposed a new energy output prediction model based on long and short-term memory network (LSTM)-Bayesian network (BN) by combining the benefits of BN in uncertainty quantification with the processing power of LSTM network to address the issue of volatility and uncertainty of new energy output. Meanwhile, by introducing a price-based demand response mechanism, users were incentivized to increase electricity consumption when the new energy generation was in excess and reduce electricity consumption during the peak period, so as to realize the flexible regulation of loads and the efficient utilization of new energy. The new energy output prediction model developed in the study had the highest degree of match between the anticipated and actual values in various data sets, as demonstrated by the experimental findings, which were above 0.99. In the Google Earth Engin and GEFCom2014 datasets, the operation solution speed was quick and stabilized after 64 and 80 iterations, respectively. Additionally, the model’s predicted and actual curve values almost matched, and the actual new energy output power predication's largest prediction error was less than 1%. The implementation of a price-based demand response approach to control customers' power consumption behavior yielded a net benefit of up to 4.45 million yuan for the customers in the target area, based on the precise prediction of new energy output power. The aforementioned findings demonstrated that the LSTM-BN-based new energy output prediction model is capable of precisely projecting new energy output and efficiently matching supply and demand through a price-based demand response mechanism to increase the rate at which new energy is consumed instantly.
Optimized wind power prediction and energy storage scheduling using genetic algorithm and backpropagation neural network Wu, Peng; Li, Zongze
International Journal of Renewable Energy Development Vol 14, No 1 (2025): January 2025
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2025.60434

Abstract

As renewable energy continues to rise in the global energy mix, wind energy is gradually increasing its share in the power system as a clean, renewable form of energy. However, the volatility and uncertainty of wind power bring new challenges to power system operation, making the need for its efficient prediction and intelligent dispatch more and more urgent. Based on this, a method combining genetic algorithm and backpropagation neural network is proposed for wind power prediction and energy storage scheduling. In this study, the improved genetic algorithm-backpropagation algorithm was generated by optimizing the weights and thresholds of the backpropagation neural network through the genetic algorithm, and optimizing the crossover and mutation processes of the genetic algorithm using similar block-order single-point crossover operator and shift mutation operator. Moreover, the improved genetic algorithm-backpropagation Neural Network wind energy prediction model was successfully constructed. Subsequently, the improved genetic algorithm was applied to search for the parameters of support vector machine and an improved genetic algorithm-support vector machine photovoltaic power generation prediction model was established. The experimental results showed that the average absolute percentage error of the improved genetic algorithm backpropagation neural network algorithm was 2.4%, and the accuracy was significantly higher than that of the traditional backpropagation neural network algorithm. The maximum photovoltaic prediction error of the autoregressive integral moving average model was about 80MW, while the photovoltaic prediction error of the improved genetic algorithm support vector machine photovoltaic prediction model was only about 12kW. In addition, the average absolute percentage error of the improved genetic algorithm support vector machine photovoltaic prediction model was only 1.53%, which was only 0.2% higher than the support vector machine prediction model. This study not only improves the stability of the power grid but also provides a practical and feasible method for realizing the large-scale application of clean energy, making a positive contribution to the sustainable development of the energy industry.
Valorization of coal fly ash for the synthesis of lithium nickel-cobalt-aluminum-iron oxide (NCAF) cathode material Yudha, Cornelius Satria; Rahmawati, Aleida Dwi; Sumarti, Ragil; Muzayanha, Soraya Ulfa; Lestari, Annisa Puji; Arinawati, Meidiana
International Journal of Renewable Energy Development Vol 14, No 2 (2025): March 2025
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2025.60829

Abstract

This study demonstrates a novel approach to high-performance cathode materials by utilizing coal fly ash as a source of Al and Fe dopants for nickel-rich layered oxides. LiNixCoyAlzFe(1-x-y-z)O2 (NCAF) materials were synthesized through a combined hydrometallurgical-solid state route, incorporating fly-ash waste-derived Al/Fe hydroxides (AFH) at various concentrations during the lithiation process. The characteristics of NCAF precursors, AFH and Ni0.8Co0.2C2O4, were thoroughly investigated. Structural analysis confirms the successful formation of single-phase materials with α-NaFeO2 structure (R-3m) up to 5% AFH content, exhibiting changes in the level of order, lattice parameters, and unit cell volume. Surface area characteristics show a transition from 38.747 m²/g to 6.52 m²/g with increasing AFH content, approaching the ideal surface area. The compositional evolution from LiNi0.8Co0.2O2 to LiNi0.66Co0.16Al0.08Fe0.10O2 maintains uniform atomic distribution. In the full-cell configuration with graphite anodes (N/P ratio: 1.2-1.3), NCAF with 5% AFH demonstrates enhanced electrochemical performance (~155 mAh/g), attributed to synergistic effects of Al-induced structural stabilization and Fe-contributed redox activity. This approach establishes a pathway for simple and low-cost battery material development while addressing industrial waste utilization.
Evaluating and analyzing the performance of PV power output forecasting using different models of machine-learning techniques considering prediction accuracy Bouakkaz, Abderraouf; Lahsasna, Adel; Gil Mena, Antonio; Haddad, Salim; Luigi Ferrari, Mario; Jiménez- Castaneda, Rafael
International Journal of Renewable Energy Development Vol 14, No 1 (2025): January 2025
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2025.60547

Abstract

Solar energy as a clean, renewable, and sustainable energy source has considerable potential to meet global energy needs. However, the intermittent and uncertain character of the solar energy source makes the power balance management a very challenging task. To overcome these shortcomings, providing accurate information about future energy production enables better planning, scheduling, and ensures effective strategies to meet energy demands. The present paper aims to assess the performance of PV power output forecasting in PV systems using various machine learning models, such as artificial neural networks (ANN), linear regression (LR), random forests (RF), and Support Vector Machines (SVM). These learning algorithms are trained on two different datasets with different time steps: in the first one, a historical weather forecast with a one hour time step, and in the second one, a dataset of on-site measurements with a 5-minute time step. To provide a reliable estimation of prediction accuracy for different learning algorithms, a k-fold cross-validation (CV) is applied. Through a comparison analysis, an assessment of the accuracy of these algorithms based on various metrics such as RMSE, MAE, and MRE is performed, providing a detailed evaluation of their performance. Results obtained from this study demonstrate that the random forest algorithm (RF) outperformed other algorithms in predicting PV output, achieving the smallest prediction error, where the best values for RMSE, MRE, MAE, and R² for the weather dataset were 0.856 W, 0.256%, 0.364 W, and 0.99999, respectively, while thevalues for RMSE, MRE, MAE, and R² for the on-site measurements dataset were 8.525 W, 11.163%, 3.922 W, and 0.99922, respectively.
Consumer preferences for solid biomass fuels for energy purposes Zlateva, Penka; Yordanov, Krastin; Murzova, Mariana; Terziev, Angel
International Journal of Renewable Energy Development Vol 14, No 1 (2025): January 2025
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2025.60473

Abstract

A specialized marketing survey was conducted across wholesale markets, manufacturing enterprises, online platforms and retail stores in Bulgarian market to analyze consumer preferences for various types of pellets. The study aims to identify key factors influencing consumer choices, with a particular focus on pellet qualities like ash content and the impact of additives on the combustion process. Statistical analysis of the survey results reveals that manufacturing enterprises are the most preferred purchase channels due to the superior quality of their products, while pellets from online platforms often receive negative feedback due to quality issues. Based on the findings, four types of pellets with and without additives are selected for further analysis: 100% coniferous; a mix of 80% coniferous and 20% deciduous; a mix of 80% coniferous and 20% sunflower and 100% sunflower pellets. To confirm the combustion characteristics of these pellet types, thermogravimetry (TG) and differential scanning calorimetry (DSC) analyses are conducted at heating rates of 5°C/min and 10°C/min up to 600°C. The analysis of variance (ANOVA) on the TG data shows significant differences in mass loss during thermal treatment between the various pellet types, demonstrating differences in efficiency and quality. The results indicate that sunflower pellets produce more ash, while wood pellets have superior combustion properties with lower ash generation. These findings highlight the need for improved consumer awareness, especially regarding the impact of pellet composition and additives on ash production. The correlation analysis of the DSC data reveals that some pellet types exhibite a high degree of similarity, suggesting they could be used interchangeably in combustion systems, while other types show significant differences due to varying raw material compositions. The study concludes that improving combustion processes requires careful selection of pellet fuels tailored to specific system needs and emphasizes the importance of better labeling and clearer information on pellet composition to enhance consumer knowledge and promote best practices in biomass fuel usage.
Application of response surface methodology to optimize the dual-fuel engine running on producer gas Nguyen, Phuoc Quy Phong; Tran, Viet Dung; Nguyen, Du; Luong, Cong Nho; Paramasivam, Prabhu
International Journal of Renewable Energy Development Vol 14, No 2 (2025): March 2025
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2025.60927

Abstract

This work develops a computational framework that optimizes the performance and emissions of a dual-fuel diesel engine running on biomass-derived producer gas as the main fuel and diesel as the pilot fuel. The study connects essential responses, brake thermal efficiency, peak combustion pressure, and emissions of nitrogen oxides (NOx), carbon monoxide (CO), and unburnt hydrocarbon (HC) with controllable factors like engine load and pilot fuel injection duration. The approach consists of simulating the impacts of these controllable inputs on engine performance, then optimization to find the optimal fuel injection pressure to balance performance and emissions. The results show that engine load considerably affects NOx emissions and brake thermal efficiency; greater loads lower CO emissions but raise HC emissions at low compression ratios. Although it had little effect on NOx emissions, fuel injection pressure was vital in balancing general engine performance. Using optimization, an optimal fuel injection pressure value of 218.5 bar was identified, thereby producing a brake thermal efficiency of 27.35% and lowering emissions to 80 ppm HC, 202 ppm NOx, and 92 ppm CO. This computational method offers a strategic means for improving the efficiency of dual-fuel engines while reducing their environmental impact, hence guiding more sustainable and effective engine operation.
Determining solutions to new economic load dispatch problems by war strategy optimization algorithm Nguyen, Hung Duc; Pham, Ly Huu
International Journal of Renewable Energy Development Vol 14, No 1 (2025): January 2025
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2025.60618

Abstract

The paper applies three cutting-edge algorithms - War Strategy Optimization Algorithm (WSO), Egret Swarm Optimization Algorithm (ESOA), and  Black Widow Optimization Algorithm (BWOA) - as potential tools to determining the optimal generation power of power plants in both the Economic Load Dispatch problem (ELD) and the New ELD problem (NELD), which incorporates renewable energy resources into the traditional power system. These algorithms underwent rigorous evaluation using various test systems with complex constraints, a multi-fuel objective function, and 24-hour load demands. In System 1, at various load levels, WSO method achieves a lower total minimum cost compared to BWOA and ESOA. Specifically, WSO outperforms BWOA and ESOA by $0.68 and $2.79 for a load of 2400 MW, by $0.49 and $4.41 for a load of 2500 MW, by $0.79 and $4.83 for a load of 2600 MW, and by $0.54 and $4.53 for a load of 2700 MW. In System 2, WSO method is less cost in a day than ESOA by $ 80.92 and BWOA by $ 46.73, corresponding to 0.39% and 0.23%, respectively. Additionally, WSO excels in response capability, providing a quicker reaction time than BWOA and ESOA across all four subcases while maintaining the same control parameters. Moreover, WSO demonstrated comparable or superior results and improved search capabilities compared to previous methods. The comparison of these results underscored WSO's effectiveness in addressing these challenges and its potential for resolving broader engineering issues beyond ELD. Ultimately, the study aimed to offer valuable insights into the role of renewable energy resources in the traditional power system, particularly in cost savings.
Assessing the role of circular economy and green innovation in mitigating carbon emissions in the Visegrad countries Takyi, Kwabena Nsiah; Gavurova, Beata; Charles, Ofori; Mikeska, Martin; Sampene, Agyemang Kwasi
International Journal of Renewable Energy Development Vol 13, No 6 (2024): November 2024
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2024.60654

Abstract

The shift towards a circular economy is an essential measure in achieving sustainable development because it seeks to separate economic expansion from resource use and environmental deterioration. To meet the European Union green deal, waste management, and the net zero emissions targets various countries are developing and adopting prudent strategies. This study investigates the dynamic affiliation between circular economy (CIR), green innovation (INV), renewable energy (REE), economic progress (GDP), and urbanisation (URB) on carbon emissions (CO2) in the Visegrad (V4) countries, comprising the Czech Republic, Hungary, Poland, and Slovakia. Using the CS-ARDL technique and quantile regression, data curation from 1990-2022 was analysed after checking for cross-sectional, unit root, and cointegration. The outcome demonstrates that circular economy, green innovation, and renewable energy had a negative effect on carbon emissions. In addition, GDP and URB had an immaterially positive influence on carbon emissions. Lastly, the quantile regression confirmed that the study provides useful information for policymakers and stakeholders in the Visegrad countries. It emphasised how important it is to take a broad approach to circular economy initiatives, support eco-friendly innovations, carry out renewable energy projects, and manage the urbanisation process well to achieve long-term economic growth and environmental health.
Analyzing the influence of structural changes on CO2 emissions in OECD countries: Employing panel cointegration techniques Kahouli, Zohra; Hasni, Radhouane; Ben Jebli, Mehdi
International Journal of Renewable Energy Development Vol 14, No 1 (2025): January 2025
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2025.60697

Abstract

Structural transformations in OECD countries significantly influence carbon dioxide (CO2) emissions, affecting economic and social dimensions. These transformations encompass changes in industrial composition, technological progress, energy consumption patterns, and policy frameworks. This research investigates the impact of such structural shifts on CO2 emissions across a panel of 38 OECD countries between 2000 and 2021, using panel cointegration techniques to ensure robust analysis. The study confirms the presence of cross-sectional dependence among countries and establishes long-run cointegration relationships. Results from Fully Modified Ordinary Least Squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS) models indicate that renewable energy, advancements in information and communication technology, and structural changes significantly reduce CO2 emissions. In contrast, economic growth, reliance on non-renewable energy, and institutional quality are linked to higher emissions. However, estimates derived from Panel-Corrected Standard Errors (PCSE) and Mean Group Panel (MGP) methods differ from those of FMOLS and DOLS, underscoring potential methodological variances in evaluating these relationships. This study highlights the pivotal role of structural changes in emission reduction strategies, while also emphasizing the importance of methodological choices in policy analysis. The findings provide valuable insights for policymakers aiming to align economic growth with environmental sustainability within OECD countries. Moreover, the research stresses the necessity of incorporating structural changes into long-term climate strategies to ensure their effectiveness. Future studies could expand the analysis by integrating more recent data and exploring non-linear relationships to refine policy recommendations further.
Application of supervised machine learning and Taylor diagrams for prognostic analysis of performance and emission characteristics of biogas-powered dual-fuel diesel engine Le, Khac Binh; Duong, Minh Thai; Cao, Dao Nam; Le, Van Vang
International Journal of Renewable Energy Development Vol 13, No 6 (2024): November 2024
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2024.60724

Abstract

In the ongoing search for an alternative fuel for diesel engines, biogas is an attractive option. Biogas can be used in dual-fuel mode with diesel as pilot fuel. This work investigates the modeling of injecting strategies for a waste-derived biogas-powered dual-fuel engine. Engine performance and emissions were projected using supervised machine learning methods including random forest, lasso regression, and support vector machines (SVM). Mean Squared Error (MSE), R-squared (R²), and Mean Absolute Percentage Error (MAPE) were among the criteria used in evaluations of the models. Random Forest has shown better performance for Brake Thermal Efficiency (BTE) with a test R² of 0.9938 and a low test MAPE of 3.0741%. Random Forest once more exceeded other models with a test R² of 0.9715 and a test MAPE of 4.2242% in estimating Brake Specific Energy Consumption (BSEC). With a test R² of 0.9821 and a test MAPE of 2.5801% Random Forest emerged as the most accurate model according to carbon dioxide (CO₂) emission modeling. Analogous results for the carbon monoxide (CO) prediction model based on Random Forest obtained a test R² of 0.8339 with a test MAPE of 3.6099%. Random Forest outperformed Linear Regression with a test R² of 0.9756% and a test MAPE of 7.2056% in the case of nitrogen oxide (NOx) emissions. Random Forest showed the most constant performance overall criteria. This paper emphasizes how well machine learning models especially Random Forest can prognosticate the performance of biogas dual-fuel engines.

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