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International Journal of Renewable Energy Development
Published by Universitas Diponegoro
ISSN : 22524940     EISSN : 27164519     DOI : https://doi.org/10.14710/ijred
Core Subject : Science,
Chemistry Energy
The scope of journal encompasses: Photovoltaic technology, Solar thermal applications, Biomass, 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 and management The journal was first introduced in February 2012 and regularly published online three times a year (February, July, October).
Arjuna Subject : Kimia(semua kategori) - Kimia (Lain-Lain)
Articles 573 Documents
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Retraction Notice to Control of Bidirectional DC-DC Converter for Micro-Energy Grid’s DC Feeders' Power Flow Application, IJRED 11(2), 533-546 Hadiyanto, H
International Journal of Renewable Energy Development Vol 12, No 6 (2023): November 2023
Publisher : Center of Biomass & Renewable Energy, Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/ijred.2023.57139

Abstract

Refers to: RETRACTED: Control of Bidirectional DC-DC Converter for Micro-Energy Grid’s DC Feeders' Power Flow Application. International Journal of Renewable Energy Development, Volume 11(2), May 2022, Pages 533-546 Muhammad Hammad Saeed, Wang Fangzong,  Basheer Ahmed Kalwar ------------------------------------------------------------------------------------------------------------------------------- 
Performance characterization of a novel PV/T panel with nanofluids under the climatic conditions of Muscat, Oman Afzal Husain; Nabeel Z. Al-Rawahi; Nasser A. Al-Azri; Mohammed Al-Naabi; Musaab El-Tahir
International Journal of Renewable Energy Development Vol 12, No 5 (2023): September 2023
Publisher : Center of Biomass & Renewable Energy, Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/ijred.2023.53287

Abstract

The study presents an experimental analysis of a novel mini channels-based Photovoltaic/Thermal (PV/T) panel with nanofluid flow. The design consists of a PV plate attached to an aluminum substrate absorber plate having minichannels grooved on it to act as a solar collector and cooling mechanism for PV. The proposed design was tested for thermal and electrical efficiencies under the working fluids of water, Al2O3, and SiO2 nanofluids at 0.1% and 0.2% concentrations in water and at a flow rate of 0.005 l/s to 0.045 l/s. The experiments were carried out outdoors in a real environment and the measurements were taken for PV surface and fluid temperatures, incidence solar flux, electrical voltage, and current produced. The PV and PV/T performance was compared, and a noticeable enhancement in electrical efficiency was observed with the proposed design as compared to the bare PV module, and an appreciable augmentation in thermal efficiency was noticed when nanofluids were applied. The maximum electrical and thermal efficiencies of PV/T with 0.2% Al2O3 nanofluid were 19.1% and 73.4%, respectively; whereas for bare PV panels, the electrical efficiency was 18.7%. The Al2O3 nanofluid at 0.2% exhibited more than a 10% increase in thermal efficiency compared to water as a working fluid in PV/T.
Unlocking Africa’s solar and wind energy potential: A panel data analysis on the determinants of the production of electricity through solar and wind energy de Vries, Martina Gintarė
International Journal of Renewable Energy Development Vol 12, No 6 (2023): November 2023
Publisher : Center of Biomass & Renewable Energy, Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/ijred.2023.52563

Abstract

With growing global concerns about and attention drawn to climate change, there is a pressing need to transition towards sustainable practices to live more harmoniously with the environment. To mitigate future climate changes, many support and pursue the uptake of renewable energy to slowly shift to a more electricity powered world. Africa, richly endowed with the potential of solar and wind, stands at a pivotal point with the opportunity to develop through electricity generated by renewable. Therefore, this research delves into the complexity of 25 factors influencing the production of solar and wind-powered electricity within the continent. Through a panel data analysis conducted for the years of 2010 till 2019, the study identifies several determinants to have positive and negative effects. Results highlight the intertwined nature of regional challenges and opportunities, emphasizing that political stability, socio-economic dynamics, sound national strategies, and environmental and international commitments play pivotal roles in determining the trajectory of solar and wind energy integration in Africa’s electricity mix. Notably the study underscores that a uniform approach across Africa is insufficient, instead tailored national and foreign strategies based on regional specifics found within this study are imperative for maximizing renewable energy adoption. 
Experimental investigation on the performance of a pyramid solar still for varying water depth, contaminated water temperature, and addition of circular fins Yuvaperiyasamy, Mayilsamy; Senthilkumar, Natarajan; Deepanraj, Balakrishnan
International Journal of Renewable Energy Development Vol 12, No 6 (2023): November 2023
Publisher : Center of Biomass & Renewable Energy, Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/ijred.2023.57327

Abstract

The experimental investigation was meant to investigate the effect of water depth in the basin, the water temperature at the inlet of solar still, and adding circular fins to the pyramid solar still on freshwater output. The investigation was divided into three sections. The first area of research is to study effect of increasing water depth in the solar still, which ranged from 2 to 6 cm, second section concentrated on varying the inflow water temperature from 30 to 50ºC, and third section investigated the influence of incorporating circular fins into the solar still basin on the water output and quality. The experimental findings showed that basin depth considerably impacts freshwater flow. The highest significant difference, 38%, was recorded by changing the water level in the basin from 2 to 6 cm. Freshwater yielded the most at a depth of 2 cm, totalling 1250.3 mL, followed by 1046 mL at a depth of 3 cm. A water depth of 4 cm produced 999 mL, whereas a water depth of 5 cm made 911 mL. The lowest production occurred at a water depth of 6 cm, producing 732 mL; furthermore, including fins at the bottom increased productivity by 8.2%. Elevating the temperature from 30 to 50ºC of the inlet water led to a water output increase of 15.3% to 22.2%. These findings underscore the profound potential of harnessing solar energy to address global water challenges and pave the way for further advancements in efficient freshwater production
Evaluating the EEMD-LSTM model for short-term forecasting of industrial power load: A case study in Vietnam Nam Nguyen Vuu Nhat; Duc Nguyen Huu; Thu Nguyen Thi Hoai
International Journal of Renewable Energy Development Vol 12, No 5 (2023): September 2023
Publisher : Center of Biomass & Renewable Energy, Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/ijred.2023.55078

Abstract

This paper presents the effectiveness of the ensemble empirical mode decomposition-long short-term memory (EEMD-LSTM) model for short term load prediction. The prediction performance of the proposed model is compared to that of three other models (LR, ANN, LSTM). The contribution of this research lay in developing a novel approach that combined the EEMD-LSTM model to enhance the capability of industrial load forecasting. This was a field where there had been limited proposals for improvement, as these hybrid models had primarily been developed for other industries such as solar power, wind power, CO2 emissions, and had not been widely applied in industrial load forecasting before. First, the raw data was preprocessed using the IQR method, serving as the input for all four models. Second, the processed data was then used to train the four models. The performance of each model was evaluated using regression-based metrics such as mean absolute error (MAE) and mean squared error (MSE) to assess their respective output. The effectiveness of the EEMD-LSTM model was evaluated using Seojin industrial load data in Vietnam, and the results showed that it outperformed other models in terms of RMSE, n-RMSE, and MAPE errors for both 1-step and 24-step forecasting. This highlighted the model's capability to capture intricate and nonlinear patterns in electricity load data. The study underscored the significance of selecting a suitable model for electricity load forecasting and concluded that the EEMD-LSTM model was a dependable and precise approach for predicting future electricity assets. The model's robust performance and accurate forecasts showcased its potential in assisting decision-making processes in the energy sector.
Photovoltaic power prediction based on sky images and tokens-to-token vision transformer Dai, Qiangsheng; Huo, Xuesong; Su, Dawei; Cui, Zhiwei
International Journal of Renewable Energy Development Vol 12, No 6 (2023): November 2023
Publisher : Center of Biomass & Renewable Energy, Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/ijred.2023.57902

Abstract

Photovoltaic (PV) power generation has high uncertainties due to the randomness and imbalance nature of solar energy and meteorological parameters. Hence, accurate PV power forecasts are essential in the operation of PV power plants (PVPP) for short-term dispatches and power generation schedules. In this paper, a new deep neural network structure based on vision transformer is proposed to combine sky images and Tokens-To-Token（T2T） for photovoltaic power prediction. The method uses an incremental tokenization module to aggregate neighboring image patches into tokens, which capture the local structural information of the clouds. Then, an efficient T2T-ViT backbone network is used to extract the global attentional relationships of the tokens for power prediction. In order to evaluate the performance of the proposed model, the method was compared with several deep learning architectures such as ResNet and GoogleNet on a dataset collected by the National Renewable Energy Laboratory in Colorado, USA. The results of power prediction were analysed using training loss, prediction error, and linear regression, and they show that the proposed method achieves higher prediction accuracy and lower error compared to the existing methods, especially in short- and ultra-short-term prediction. The paper demonstrates the potential of applying Transformer models to computer vision tasks for renewable energy forecasting. The results show that the proposed method achieves higher prediction accuracy and lower error than several deep learning architectures, such as ResNet and GoogleNet, especially in short- and ultra-short-term prediction.
Optimization and management of flare gases using 4R procedure based on 3E structures: Simulation and optimization of knock-out drum high-pressure flares Ahmadzadeh, Ali
International Journal of Renewable Energy Development Accepted Articles
Publisher : Center of Biomass & Renewable Energy, Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/ijred.2024.56524

Abstract

In this paper, the optimization of the knock out drum is to create a shell around it and inject water steam into the shell so that a uniform temperature distribution can be created inside the drum, and as a result, freezing does not occur and liquid drops inside the burner do not burn. The result of the simulations showed that in the drainage part of the drum, humidity associated with inlet gas freezes upon entering the drum after pressure and temperature drop suddenly. In the drainage part of the drum and enter water steam with a temperature 438 K and relative pressure 550000 Pa, the freezing of the coating part of the drum is eliminated and finally the water steam with liquid water which is caused by the heat transfer between the steam and bottoms of drum is out from drainage part of drum. Firstly, simulating the drum to prove the electric heater's insufficient power, and secondly simulating the drum and its surrounding coating to eliminate the freezing zone.
Building energy consumption prediction method based on Bayesian regression and thermal inertia correction Su, Huiling; Duan, Meimei; Zhuang, Zhong; Bai, Yunlong
International Journal of Renewable Energy Development Vol 13, No 1 (2024): January 2024
Publisher : Center of Biomass & Renewable Energy, Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/ijred.2024.58012

Abstract

The accurate prediction of building energy consumption is a crucial prerequisite for demand response (DR) and energy efficiency management of buildings. Nevertheless, the thermal inertia and probability distribution characteristics of energy consumption are frequently ignored by traditional prediction methods. This paper proposes a building energy consumption prediction method based on Bayesian regression and thermal inertia correction. The thermal inertia correction model is established by introducing an equivalent temperature variable to characterize the influence of thermal inertia on temperature. The equivalent temperature is described as a linear function of the actual temperature, and the key parameters of the function are optimized through genetic algorithm (GA). Using historical energy usage, temperature, and date type as inputs and future building energy comsuption as output, a Bayesian regression prediction model is established. Through Bayesian inference, combined with prior information on building energy usage data, the posterior probability distribution of building energy usage is inferred, thereby achieving accurate forecast of building energy consumption.  The case study is conducted using energy consumption data from a commercial building in Nanjing. The results of the case study indicate that the proposed thermal inertia correction method is effective in narrowing the distribution of temperature data from a range of 24.5°C to 36.5°C to a more concentrated range of 26.5°C to 34°C, thereby facilitating a more focused and advantageous data distribution for predictions. Upon applying the thermal inertia correction method, the relative errors of the Radial Basis Function (RBF) and Deep Belief Network (DBN) decreases by 2.0% and 3.1% respectively, reaching 10.9% and 7.0% correspondingly. Moreover, with the utilization of Bayesian regression, the relative error further decreases to 4.4%. Notably, the Bayesian regression method not only achieves reduced errors but also provides probability distribution, demonstrating superiority over traditional methods.
Enhancing microbial fuel cell performance with carbon powder electrode modifications for low-power sensors modules Al-badani, Mohammed Adel; Chong, Peng Lean; Lim, Heng Siong
International Journal of Renewable Energy Development Vol 13, No 1 (2024): January 2024
Publisher : Center of Biomass & Renewable Energy, Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/ijred.2024.58977

Abstract

Microbial Fuel Cell (MFC) is a promising technology for harnessing energy from organic compounds. However, the low power generation of MFCs remains a significant challenge that hinders their commercial viability. In this study, we reported three distinct modifications to the stainless-steel mesh (SSM), carbon cloth, and carbon felt electrodes using carbon powder (CP), a mixture of CP and ferrum, and a blend of CP with sodium citrate and ethanol. The MFC equipped with an SSM and CP anode showed a notable power density of 1046.89 mW.m-2. In comparison, the bare SSM anode achieved a maximum power density of 145.8 mW m-2. Remarkably, the 3D-modified SSM with a CP anode (3D-SSM-CP) MFC exhibited a substantial breakthrough, attaining a maximum power density of 1417.07 mW m-2. This achievement signifies a significant advancement over the performance of the unaltered SSM anode, underscoring the effectiveness of our modification approach. Subsequently, the 3D-SSM-CP electrode was integrated into single-chamber MFCs, which were used to power a LoRaWAN IoT device through a power management system. The modification methods improved the MFC performance while involving low-cost and easy fabricating techniques. The results of this study are expected to contribute to improving MFC's performance, bringing them closer to becoming a practical source of renewable energy.
Grey wolf optimization and incremental conductance based hybrid MPPT technique for solar powered induction motor driven water pump Shetty, Divya; Sabhahit, Jayalakshmi Narayana
International Journal of Renewable Energy Development Vol 13, No 1 (2024): January 2024
Publisher : Center of Biomass & Renewable Energy, Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/ijred.2024.57096

Abstract

The use of Solar Powered Water Pumps (SPWP) has emerged as a significant advancement in irrigation systems, offering a viable alternative to electricity and diesel-based pumping methods. The appeal of SPWPs to farmers lies in their low maintenance costs and the incentives provided by government agencies to support sustainable and cost-effective agricultural practices. However, a critical challenge faced by solar photovoltaic (PV) systems is their susceptibility to power loss under partial shading conditions, which can persist for extended periods, ultimately reducing system efficiency. To address this issue, this paper proposes the integration of Maximum Power Point Tracking (MPPT) controllers with efficient algorithms designed to identify the peak power during shading events. In this study, a hybrid approach combining Grey Wolf Optimization (GWO) and Incremental Conductance (INC) is employed to maximize the power output of SPWPs driven by an induction motor under partial shading conditions. In order to achieve faster convergence to the global peak, GWO handles the first stages of MPPT and then INC algorithm is employed at the end of the MPPT process.  This method reduces the computations of GWO and streamlines the search space. The paper evaluates the performance of the induction motor in terms of speed settling time and torque ripple. To validate the effectiveness of the GWO-INC hybrid approach, simulations are conducted using the MATLAB Simulink platform. The outcomes are then compared with results obtained from various well-known approaches, including Particle Swarm Optimization – Perturb and Observe (PSO-PO), PSO-INC, and GWO-PO, illustrating the superiority of the GWO-INC hybrid approach in enhancing the efficiency and performance of solar water pumps during shading. The GWO-INC excels with 99.6% accuracy in uniform shading and 99.8% in partial shading. It achieves convergence in a mere 0.55 seconds under uniform shading conditions and only 0.42 seconds when partial shading is present. Moreover, it significantly reduces torque oscillations, with a torque ripple of  8.26% in cases of uniform shading and 10.56% in partial shading.

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