Building thermal load prediction method based on heat storage system

Abstract

The invention discloses a building thermal load prediction method based on a heat storage system. The building thermal load prediction method comprises the following steps: obtaining physical parameters of a building; acquiring a target temperature required by the building and a corresponding time period; obtaining meteorological parameters of the building at each moment; acquiring internal heat source parameters of the building at each moment; building a prediction model by taking the physical parameters, the target temperature, the corresponding time period, the meteorological parameters andthe internal heat source parameters of the building as input quantities and taking the building heat load as an output quantity; and substituting the parameters obtained according to the above stepsinto a prediction model to obtain the building thermal load. According to the technical scheme, the thermal load prediction model is established to obtain hourly thermal load and daily heat demand inthe building heating season, the solid electric heat storage-electric direct heating combined heating equipment power ratio optimization model is established, and the economic optimization in the equipment operation life is taken as the target, the optimal input power ratio of the solid electric heat storage equipment to the electric direct heating equipment is determined, the equipment utilization rate is increased, and meanwhile the equipment initial investment and operation cost are reduced.

Description

technical field [0001] The disclosure of the present invention relates to the field of building heat load forecasting, in particular to a building heat load forecasting method based on a thermal storage system. Background technique [0002] In recent years, my country has vigorously promoted and encouraged electric energy substitution, which has become an important national strategy and an important starting point for adjusting my country's energy structure. With the introduction of the energy development strategy, a series of electricity price systems such as peak-valley time-of-use electricity prices and expanding the peak-valley electricity price difference are also being fully implemented. The regenerative electric boiler is able to make full use of low-valley electricity to achieve peak-shaving and valley-filling. The market prospect in the replacement project is good. [0003] Heat load forecasting on the energy side plays an important role in the planning and design ...

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Problems solved by technology

[0008] For areas that enjoy preferential policies for peak and valley electricity prices, electric heat storage heating is the best choice. However, since most projects use the index method in heat load prediction, the predicted load is often higher than the actual load, especially in the early stage of heating and heating. In the later stage, when selecting solid hea

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