WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7150

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Access Right: Open AccessSubject: search.filters.subject.Renewable Energy

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  • Article
    Univariate Deep Learning Models for Short-Term Electricity Load Forecasting from Renewables
    (Ankara University, Faculty of Science, 2025) Kabran, Fatma Basoglu; Unlu, Kamil Demirberk
    Renewable energy offers a cost-effective, carbon-free solution for energy needs, while protecting the environment. Accurate forecasting of electricity generation from renewable sources is crucial for the efficiency of modern power grids. This study employs a univariate deep learning approach to predict daily renewable energy generation, evaluating Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) as candidate models. Five performance metrics-mean absolute error, root mean squared error, mean absolute percentage error, mean absolute scaled error and the coefficient of determination-are employed to assess the forecasting power of the algorithms. The empirical results show that CNN outperforms other models, achieving an R2 of almost 94%. This research shows that the univariate model based on historical data of electricity load generated from renewables can accurately predict day-ahead electricity load, even without meteorological data.
  • Article
    Citation - WoS: 2
    Citation - Scopus: 3
    Emerging Trends of Biohydrogen Ecosystem on Environmental Sustainability: a Case Study
    (Elsevier Sci Ltd, 2025) Goren, A. Yagmur; Dincer, Ibrahim
    The greatest threat to humanity is now considered climate change. Biomass as a renewable energy source is treated as one of the clean energy sources that help meet humanity's energy needs. In the transition to a new energy system based on renewable energies, biomass can be crucial. This paper particularly focuses on a new biohydrogen (bioH2) ecosystem development concept for communities to provide global and local sustainable and green energy, considering the biomass-to-bioenergy nexus. In this regard, the paper further discusses the different bioH2 ecosystem concepts and emerging trends where biomass and renewable resources are utilized for energy production. In addition, the bioenergy production potentials of different agricultural crop wastes are evaluated for different end-use purposes like electricity, heat, cogeneration, and transport. In parallel to its high bioenergy yield, the highest total energy (83,686.8 GJ) and gross electricity (4686.5 MWh) production values were observed for the olive cake waste. Moreover, the biomethane and bioethanol production potentials of the crop wastes are evaluated. The highest biomethane yield of 253.7 m3/ha with a total bioenergy production of 40,662.6 GJ was obtained for the maize stover waste, while its bioethanol production was 505.7 L/ha. Consequently, the bioH2 ecosystem with biomass utilization reveales as a sustainable and green way of providing future energy for communities owing to the great potential of crop wastes for bioenergy production.