Abstract: A control method for a phase-change energy storage type cold and heat combined supply system with a multi-heat source heat pump includes the following steps of: recording the temperature of a solar panel using solar energy as a heat source at a certain moment as tt, the temperature of a water source to be taken as ts, the temperature of a geothermal source as td, the temperature of an air source as tk; calculating the heat storage efficiency ?s, ?k and ?d of the phase change material under the action of solar energy-water energy, solar energy-air source and solar energy-ground source; calculating the hourly heat storage capacity of the heat pump Qrb, comparing it with the total hourly heat consumption of the system Qtol, and if Qrb<1.45Qtol, regulating the use of various heat sources through the heat storage integrated regulation system until Qrb<1.45Qtol.

Abstract: According to a transformer-architecture-based method and system for IoT for and smart control of urban integrated energy, urban integrated energy IoT information acquired by a terminal from different areas is output to a server after being standardized into a sequence signal, where energy demand prediction is performed through learning of a deep learning network configured on the server, a smart control network is configured and built by using the deep learning network, real-time prediction processing is performed on the urban integrated energy IoT information acquired in real time, and a result is uploaded to a workstation for reviewing. Based on the transformer architecture, the deep learning network is combined with a fast Fourier transform algorithm and inverse transform on a sequence, and an FFT-Attention mechanism is proposed. Compared with a conventional transformer architecture, frequency domain information of a sequence is given more emphasis.

Abstract: A deep learning-based method for fusing multi-source urban energy data and a storage medium are provided to perform data fusion on multi-source urban energy data found in big data and perform multi-scale and multimodal information fusion by using a cross-modal transformer, thereby implementing cross-modal mutual fusion of multi-source heterogeneous types of data to obtain a fused feature for prediction of a quantity of energy that will be used in the future and a quantity of energy that needs to be produced. The present disclosure proposes a multi-scale cooperative multimodal transformer architecture to enhance an effect of representation learned from an unaligned multimodal sequence. Not only there is a higher degree of correlation in multi-source urban energy data fusion, but also a system becomes more lightweight.

Abstract: A deep learning-based method for fusing multi-source urban energy data and a storage medium are provided to perform data fusion on multi-source urban energy data found in big data and perform multi-scale and multimodal information fusion by using a cross-modal transformer, thereby implementing cross-modal mutual fusion of multi-source heterogeneous types of data to obtain a fused feature for prediction of a quantity of energy that will be used in the future and a quantity of energy that needs to be produced. The present disclosure proposes a multi-scale cooperative multimodal transformer architecture to enhance an effect of representation learned from an unaligned multimodal sequence. Not only there is a higher degree of correlation in multi-source urban energy data fusion, but also a system becomes more lightweight.

Abstract: According to a transformer-architecture-based method and system for IoT for and smart control of urban integrated energy, urban integrated energy IoT information acquired by a terminal from different areas is output to a server after being standardized into a sequence signal, where energy demand prediction is performed through learning of a deep learning network configured on the server, a smart control network is configured and built by using the deep learning network, real-time prediction processing is performed on the urban integrated energy IoT information acquired in real time, and a result is uploaded to a workstation for reviewing. Based on the transformer architecture, the deep learning network is combined with a fast Fourier transform algorithm and inverse transform on a sequence, and an FFT-Attention mechanism is proposed. Compared with a conventional transformer architecture, frequency domain information of a sequence is given more emphasis.