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• Solar assisted systems have a high energy saving potential for building services. • Energetic and economic figures of solar and conventional systems were compared. • Solar PV assisted systems saved 39% to 100% of primary energy depending on location. • Solar PV assisted systems surpass solar thermal both technically and economically. Solar thermal and Photovoltaic (PV) systems can save significant amount of non-renewable energy that are utilized to satisfy the energy services for buildings. The falling prices of PV in recent years increased the attractiveness of PV systems. The purpose of this research is to develop a systematic methodology for fair technical and economical comparison of reference and solar assisted systems. For this reason, the annual energy requirements for heating, cooling and DHW for a selected dormitory building under five representative climatic conditions were evaluated using the Hourly Analysis Program (HAP). At the same time, solar thermal and PV systems have been designed and simulated for the same building using Sketchup, Trnsys and PVsyst. Finally, a systematic comparison between the solar systems and five conventional systems have been carried out in terms of primary energy ratio, and Levelized Cost Of Energy (LCOE). For the case of Amman, solar thermal and electric systems achieved a non-renewable primary energy savings of 29% and 100%, respectively, whereas, they achieved LCOE savings of 15% and 62%, respectively compared to the best conventional system. The selection of the best solution for a given project requires a full understanding of the system performance and the interaction of its components rather than the technology efficiency only. This system performance depends on the building envelope, load patterns, availability of solar radiation, roof area availability, energy prices, and policies. Thus, a case-by-case analysis should be done for each project in each climatic region. [ABSTRACT FROM AUTHOR]

• LFG varies in species content and concertation based on the original waste material. • Optimizing the LFG combustion is required to maximize the landfill power output. • Real data from Al Ghabawi landfill was used as a model input. • A combustion model and ANN was used to optimize the landfill's operating parameters. • The intake pressure and the ignition timing were significant in the optimization. • Landfill's power output can be increased by around 1 MW for each genset. Landfills have high potency as renewable energy sources by producing biogas from organic waste degradation. Landfills biogas (LFG) can be used for power plant purposes instead of allowing it to flare to the atmosphere which contributes to the global warming. The aim of this work was to introduce and examine an optimization model for maximizing the power generation of Al Ghabawi landfill in Amman city, Jordan. The optimization process focused on studying the effect of several operating parameters within the landfill power plant. To achieve this goal, a combustion model had been built and validated against a set of historical real data obtained from the landfill operator. In addition to that, an Artificial Neural Network (ANN) model had been built to perform a multi-objective optimization to obtain the optimal power generation conditions for Al Ghabawi landfill. The combustion model along with the ANN model aim to estimate the best engine operating conditions based on the actual daily data of the landfill. The engine operating parameters includes the intake pressure and temperature, the ignition time and the equivalence ratio. The results of the study indicate that the current operating parameters can be optimized to maximize the gensets power generation. Based on the daily data of the produced LFG, the optimal operating conditions for the landfill are 2.32 bar for the intake pressure, 303 K for the intake temperature, 0.9–1.0 for the equiveillance ratio and for the ignition time it is 13 degrees before the top dead center (BTDC). These optimized operating parameters can maximize the landfill power generation by at least 1 MW for each genset. [ABSTRACT FROM AUTHOR]