@article{ElMakroumKhallaayounLghouletal.2023, author = {El Makroum, Reda and Khallaayoun, Ahmed and Lghoul, Rachid and Mehta, Kedar and Z{\"o}rner, Wilfried}, title = {Home Energy Management System Based on Genetic Algorithm for Load Scheduling: A Case Study Based on Real Life Consumption Data}, volume = {16}, pages = {2698}, journal = {Energies}, number = {6}, publisher = {MDPI}, address = {Basel}, issn = {1996-1073}, doi = {https://doi.org/10.3390/en16062698}, year = {2023}, abstract = {This paper proposes a home energy management system able to achieve optimized load scheduling for the operation of appliances within a given household. The system, based on the genetic algorithm, provides recommendations for the user to improve the way the energy needs of the home are handled. These recommendations not only take into account the dynamic pricing of electricity, but also the optimization for solar energy usage as well as user comfort. Historical data regarding the times at which the appliances have been used is leveraged through a statistical method to integrate the user's preference into the algorithm. Based on real life appliance consumption data collected from a household in Morocco, three scenarios are established to assess the performance of the proposed system with each scenario having different parameters. Running the scenarios on the developed MATLAB script shows a cost saving of up to 63.48\% as compared to a base scenario for a specific day. These results demonstrate that significant cost saving can be achieved while maintaining user comfort. The addition of supplementary shiftable loads (i.e., an electric vehicle) to the household as well as the limitations of such home energy management systems are discussed. The main contribution of this paper is the real data and including the user comfort as a metric in in the home energy management scheme.}, language = {en} } @article{RoueguiBellabairElAslietal.2025, author = {Rouegui, Meryem and Bellabair, Hind and El Asli, Abdelghani and Amar, Amine and Z{\"o}rner, Wilfried and Rachidi, Fouad and Lghoul, Rachid}, title = {Bio-Methanization of Sheep Manure and Beet Waste in the Meknes-F{\`e}s Region, Morocco: Effects of Pretreatment and Machine Learning Applications for Biochemical Methane Potential Prediction}, volume = {10}, pages = {213}, journal = {Recycling}, number = {6}, publisher = {MDPI}, address = {Basel}, issn = {2313-4321}, doi = {https://doi.org/10.3390/recycling10060213}, year = {2025}, abstract = {Sheep manure and beet waste (the uneatable leaf part of the beet) are promising feedstock for biogas production due to their abundance and organic richness. However, their high lignocellulosic content reduces anaerobic digestibility and controls methane yield. This study investigates the effect of various pretreatment strategies, namely physical, thermal, and combined physical-thermal methods, on the Biochemical Methane Potential (BMP) of sheep manure and beet waste. Batch anaerobic digestion experiments were conducted under mesophilic conditions, with BMP values recorded for each treatment. The highest BMP for sheep manure, 125 Nml CH4/g VS, was achieved using combined physical and thermal pretreatment. This approach enhanced methane production by 16\%, 25\%, and 60\% compared to physical pretreatment (PP) alone, thermal pretreatment (TP) alone, and no pretreatment, respectively, while the one BMP for beet waste is 80 Nml CH4/g VS and obtained with thermal pretreatment. To predict BMP outcomes, three machine learning approaches are applied, namely Linear Regression (LM), Random Forest Regression (RFR), and Gradient Boosting Machine (GBM), using digestion time (N days), total solids (Ts), volatile solids (Vs), pretreatment type, and biomass type. The variance analysis confirmed that the interaction between pretreatment and biomass type significantly improved model performance. While diagnostic checks revealed non-linear patterns limiting the linear model, ensemble methods achieved stronger results. The RFR model explained 79.5\% of the variance with a Root Mean Square Error (RMSE) of about 15.7, whereas the GBM model achieved the lowest RMSE of 5.05. GBM captures complex non-linear interactions. In addition, variable importance analyses identified digestion time, solid content, and pretreatment as the most influential factors for methane yield, with the combined chemical and physical pretreatment producing the highest biogas outputs. These findings underscore the potential of advanced machine learning models, particularly GBM (Gradient Boosting Machine), for optimizing anaerobic digestion strategies and maximizing biogas recovery from sheep manure and beet waste.}, language = {en} }