by Peter Ebhodaghe Akhator  and Ufuoma Georgina Unueroh

Received: 16 February 2026;  Published: 15 May 2026;  doi: 10.21926/jept.2602009

Waste-derived briquettes offer a promising alternative to fossil fuels and are characterised by low production costs, high volumetric calorific value, robust mechanical strength, and excellent durability. To optimize their adoption, four variants of waste-derived briquettes were produced with different mass ratios of sawdust to polyethylene terephthalate (PET) plastic (100:0, 90:10, 80:20, and 70:30) using high-pressure compaction without binders. Thermogravimetric analysis (TGA) and the Coats-R [...]

by G.-Fivos Sargentis  , Nikolaos Papadodimas  , Ioannis Benekos  , Nikolaos M. Katsoulakos  , Panayiotis Dimitriadis  , Nikos Tepetidis  , Romanos Ioannidis  , Ilias Arvanitidis  , Marios Athanasios Angelidis  , Danai Saperopoulou  , Georgios David Laoutaris  , Matthaios Maravelakis  , Orestis I. Amiralis  , David Markantonis  , Athanasia Alexandridou  , Nikos Mamassis  and Demetris Koutsoyiannis

Received: 20 December 2025;  Published: 20 April 2026;  doi: 10.21926/jept.2602008

The increasing penetration of renewable energy sources (RES) in the energy mix, particularly solar photovoltaic and wind power, poses significant challenges to electricity grid reliability due to their inherent stochastic variability. This study develops a stochastic framework to assess the ability of RES to balance electricity demand, with a focus on storage requirements and reliability implications. Using North Euboea, Greece, as a representative case study, normalized hourly time series of el [...]

by Leonardo Alves Messias  , José Luis Domingos  , Thiago Augusto Mendes  , Bruno Barzellay Ferreira da Costa  , Ana Carolina Fernandes Maciel  , Saymon Fonseca Santos Mendes  and Raphael de Aquino Gomes

Received: 17 October 2025;  Published: 03 April 2026;  doi: 10.21926/jept.2602007

The increasing demand for renewable energy sources has intensified the adoption of photovoltaic systems. This study proposes predictive models for solar power generation that operate without dependence on on-site meteorological stations. The proposed approach integrates generation data from geographically distributed plants and accounts for the distance to meteorological stations when constructing climatic variables. Two machine learning techniques, Random Forest (RF) and Long Short-Term Memory [...]