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The metering mode is gradually changing from volumetric metering to energy metering under the open-access operation of gas pipelines. The gas component tracking by operating simulation is an effective method to realize the energy metering of gas networks. In this paper, coupling and decoupling transient component tracking models of gas networks are proposed. The pipeline governing equations, compressor equations, valve equations, and node relationship equations are considered in the proposed models. The implicit central difference method and the damped Newtonian method are adopted to solve the models. The coupling model considers the component tracking part and hydraulic-thermal simulation as a whole, while the decoupling model splits the whole process into the gas component tracking part, the parameters calculation part, and the hydraulic-thermal simulation part. In the component tracking part, the spatiotemporal variation of the gas mass fraction and density can be obtained through the input velocity of each node. Then, the parameters in the gas state equation can be solved, the hydraulic-thermal simulation can be performed, and the new velocity of each node can be calculated. The iteration for each time step ends if the deviation of flow velocity in the component tracking process and the hydraulic-thermal process satisfies the requirements. Two cases are studied to verify the effectiveness of the proposed models, and the boundary condition adaptability of the transient simulation models is analyzed. The proposed models can efficiently simulate the spatiotemporal variation of the operating variables and gas components, providing a reference in the operation of natural gas pipeline networks. [Display omitted] • The most accurate gas state equation with 11 parameters is chosen in the models. • A coupling model integrating the T-H process and the GCT process is proposed. • The T-H and GCT sub-models are decoupled to establish the decoupling model. • The proposed models are applied to different boundary conditions combinations. [ABSTRACT FROM AUTHOR]

An accurate characterization and prediction of heat loads in buildings connected to a District Heating (DH) network is crucial for the effective operation of these systems. The high variability of the heat production process of DH networks with low supply temperatures and derived from the incorporation of different heat sources increases the need for heat demand prediction models. This paper presents a novel data-driven model for the characterization and prediction of heating demand in buildings connected to a DH network. This model is built on the so-called Q-algorithm and fed with real data from 42 smart energy meters located in 42 buildings connected to the DH in Tartu (Estonia). These meters deliver heat consumption data with a 1-h frequency. Heat load profiles are analysed, and a model based on supervised clustering methods in combination with multiple variable regression is proposed. The model makes use of four climatic variables, including outdoor ambient temperature, global solar radiation and wind speed and direction, combined with time factors and data from smart meters. The model is designed for deployment over large sets of the building stock, and thus aims to forecast heat load regardless of the construction characteristics or final use of the building. The low computational cost required by this algorithm enables its integration into machines with no special requirements due to the equations governing the model. The data-driven model is evaluated both statistically and from an engineering or energetic point of view. R2 values from 0.70 to 0.99 are obtained for daily data resolution and R2 values up to 0.95 for hourly data resolution. Hourly results are very promising for more than 90% of the buildings under study. • Data driven model for heat-load prediction of buildings is developed. • Real data from 42 buildings connected to the district-heating in Tartu is used. • Different building typologies with different energy demand profiles are used. • The present model is applied to hourly and daily data. • R2 for hourly data range between 0.47 and 0.95 and for daily data range between 0.70 and 0.99 [ABSTRACT FROM AUTHOR]