Poultry litter represents a promising feedstock for biogas production through anaerobic digestion (AD), offering potential benefits for both on-farm energy supply and organic waste management. This opportunity is particularly relevant in resource-constrained countries, where limited access to reliable energy and inadequate waste management remain critical challenges. This study investigates the integration of poultry litter-based biogas production into a decentralized energy system supplying a poultry farm and a nearby household in Yaoundé, Cameroon. A techno-economic optimization framework based on mixed-integer linear programming is used to determine the cost-optimal configuration of the energy system. The results show that anaerobic digesters are only selected when constraints on poultry litter disposal are introduced. Total annual system costs increase from approximately 2680 EUR·y⁻¹ in the unconstrained scenario to 3720 EUR·y⁻¹ when up to 50% of the poultry litter is valorized locally through AD. Increasing biogas production primarily substitutes liquefied petroleum gas (LPG) used for heating and progressively reduces electricity purchases from the grid. Overall, the analysis indicates that anaerobic digestion is currently not economically competitive when evaluated solely on energy supply benefits, mainly due to the high capital cost of digesters. However, when waste management objectives or external investment support are considered, poultry litter-based biogas systems can contribute to integrated energy–waste management strategies and support circular resource use in small-scale agricultural systems.

The heating and cooling sector accounts for nearly half of Europe’s energy consumption and remains heavily dependent on fossil fuels, emphasizing the urgent need for decarbonization. Simultaneously, the global shift toward renewable energy is accelerating, alongside growing interest in decentralized energy systems where prosumers play a significant role. In this context, district heating and cooling networks, serving nearly 100 million people, are strategically important. In next-generation systems, thermal prosumers can feed-in locally produced or industrial waste heat into the network via bidirectional substations, allowing energy flows in both directions and enhancing system efficiency. The complexity of these networks, with numerous users and interacting heat flows, requires advanced predictive models to manage large volumes of data and multiple variables. This work presents the development of a predictive model based on artificial neural networks (ANNs) for forecasting excess thermal renewable energy from a bidirectional substation. The numerical model of a substation prototype designed by ENEA provided the physical data for the ANN training. Thirteen years of simulation results, combined with extensive meteorological data from ECMWF, were used to train and to test a multi-step ANN capable of forecasting the six-hour thermal power feed-in horizon using data from the preceding 24 h, improving operational planning and control strategies. The ANN model demonstrates high predictive capability and robustness in replicating thermal power dynamics. Accuracy remains high for horizons up to six hours, with MAE ranging from 279 W to 1196 W, RMSE from 662 W to 3096 W, and R² from 0.992 to 0.823. Overall, the ANN satisfactorily reproduces the behavior of the bidirectional substation even over extended forecasting horizons.