Vol. 4 No. 2 (2026)

Published: 2026-01-08

Article

  • Open Access

    Article ID: 560

    Design and Experimental Evaluation of Electromagnetic Energy-Harvesting Speed Humps for Sustainable Urban Transportation

    by Wasan Theansuwan, Surachai Hemhirun
    Clean Energy Science and Technology, Vol.4, No.2, 2026;
    112 Views
    DOI: https://doi.org/10.18686/cest560

    This study presents the design and experimental evaluation of an electromagnetic energy-harvesting speed hump (EHSH), developed to capture vehicular kinetic energy and convert it into usable electrical power. The system incorporates a rack-and-pinion mechanism coupled with a flywheel and permanent-magnet generator to ensure efficient torque transfer and energy storage. Experimental trials were performed with vehicles weighing 1100 kg, 1500 kg, and 2300 kg, operated at speeds ranging from 3 to 12 km/h. The resulting power outputs were recorded in terms of rotational speed, voltage, current, and harvested power, with comparative analysis between front- and rear-axle loading conditions. The results show that higher vehicle weights and speeds significantly increase energy output, with rear wheels generating slightly higher values than front wheels. Recent literature highlights that EHSH systems can achieve average outputs between 9–20 W in field applications and up to 85% conversion efficiency with optimized permanent-magnet linear generators. The findings of this work confirm the potential of EHSHs as sustainable urban infrastructure solutions, while also identifying challenges of fluctuating performance under diverse traffic conditions. This research contributes to ongoing efforts toward integrating renewable energy systems into road safety devices and smart city applications.

  • Open Access

    Article ID: 533

    Energy demand forecasting using deep models and autoencoder- transformer

    by Zohreh Dorrani
    Clean Energy Science and Technology, Vol.4, No.2, 2026;
    101 Views
    DOI: https://doi.org/10.18686/cest533

    This study evaluates five prominent deep learning models—CNN-LSTM, Bidirectional LSTM, GRU, Transformer, and the proposed Deep Autoencoder-Transformer for the task of energy demand forecasting. Accurate prediction of energy demand is essential for optimizing consumption and maintaining power grid stability amidst increasing complexity and multivariate data characteristics. While previous research has predominantly assessed more traditional models such as LSTM and GRU, this research fills an important gap by thoroughly comparing these with the Transformer and a novel hybrid autoencoder-Transformer model. The models were systematically trained on multivariate inputs after comprehensive preprocessing and evaluated using statistical metrics including MAE, RMSE, MAPE, and coefficient of determination (R2). The findings demonstrate that the Deep Autoencoder-Transformer model outperforms all other architectures, achieving the lowest error rates (MAE = 8.5, RMSE = 10.75, MAPE = 3.46%) and highest explanatory power (R2 = 0.991). The Transformer also achieves strong performance (MAE = 10.14, R2 = 0.988), reflecting its ability to model long-term dependencies effectively. GRU and Bidirectional LSTM models follow, balancing accuracy and computational efficiency, while CNN-LSTM, despite its combined spatial and temporal feature extraction abilities, shows comparatively lower precision likely due to architectural limitations with long-range temporal modeling. This study highlights the superior capability of attention-based Transformer architectures, especially when combined with deep autoencoding, to capture complex temporal patterns in multivariate energy data. It offers a scalable and systematic framework for benchmarking deep learning models applicable to energy demand forecasting. These insights are valuable to energy system operators and policymakers for selecting appropriate machine learning models, with the hybrid Deep Autoencoder-Transformer emerging as a promising solution for more accurate, long-horizon, multi-step forecasting in intelligent energy systems.

  • Open Access

    Article ID: 555

    Sustainability of coffee farms: Case study of the cooperativa agraria Cafetalera La Prosperidad de Chirinos

    by Franklin Hitler Fernandez-Zarate, Malluri Goñas, Jhon Oblitas, Jorge Fernandez, Darwin Gomez-Fernandez, Nilter García-Chimbo, Michael Montalvan, Lenin Quiñones-Huatangari, Rubén Eusebio Acosta-Jacinto, Milton Ríos-Julcapoma, Guillermo Guardia, Alberto Sanz-Cobeña, Annick Estefany Huaccha-Castillo, Manuel Emilio Milla-Pino
    Clean Energy Science and Technology, Vol.4, No.2, 2026;
    32 Views
    DOI: https://doi.org/10.18686/cest555

    Ignorance of the sustainability of coffee systems compromises the continuity of productive activities by weakening their economic viability, environmental integrity and social cohesion over time, which is why it is essential to carry out diagnoses. This study aimed to assess the sustainability level of coffee farms associated with the Cooperativa Agraria Cafetalera La Prosperidad de Chirinos. From January to March 2024, data were collected from 60 farms out of a population of 788. The analysis was based on nine criteria: six environmental (soil quality, crop health, solid waste and effluent management, integrated pest and disease management, ecological knowledge, and agricultural system), two economic (agricultural economy and food sovereignty), and one social (social aspects). To identify groups of farmers with homogeneous characteristics, a cluster analysis was performed and the level of sustainability of each group was determined by calculating overall averages, represented through Amoeba charts. Results identified two farm types farms in group 1 showed less sustainability than group 2, mainly due to unfavorable conditions related to soil quality. Consequently, it is recommended to to implement cover crops, live barriers, infiltration ditches, contour planting, and productive diversification for food security are recommended. This study provides a scientific diagnosis of sustainability levels on coffee farms and offers practical options for improving sustainability.