Hygro-thermal model for estimation of demand response flexibility of closed refrigerated display cabinets

Månsson, Tommie; Kalagasidis, Angela Sasic; Ostermeyer, York

doi:10.1016/j.apenergy.2020.116381

Cited by 3 publications

(1 citation statement)

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“…Theoretically, it is considered that the factors affecting the total energy consumption demand mainly include terminal energy consumption, energy processing and conversion loss, energy transportation and management process loss. The total energy consumption data of the city from 2000 to 2010 are selected as the sample data of the trend extrapolation model [8]. The model uses not the original data column, but the generated data column.…”

Section: Experimental Analysismentioning

confidence: 99%

A Grey Forecasting Model for Energy Demand Based on Optimized BP Neural Networks

Cheng,

2024

Advances in Transdisciplinary Engineering

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In order to accurately evaluate regional energy consumption and demand, the prediction model based on optimized Grey Neural Network was discussed in this paper. Considering the advantages of grey prediction model and artificial neural network model, BP neural network model was introduced to analyze the model, and genetic algorithm was used to optimize the network weight design of series model. Based on the consumption data of urban residents in a province from 2009 to 2019, the case analysis adopted the improved prediction model to test the actual demand trend of energy in the future. The results showed that the combined prediction model can reflect the development trend of energy consumption and demand in a period of time, and it had better prediction accuracy than the single prediction model. Therefore, it was feasible to apply it to complex nonlinear systems.

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Section: Experimental Analysismentioning

confidence: 99%