Background:
Energy conservation has always been a major issue in our country, and the air conditioning
energy consumption of buildings accounts for the majority of the energy consumption of buildings. If the building load
can be predicted and the air conditioning equipment can respond in advance, it can not only save energy, but also extend
the life of the equipment.
Introduction:
The Neural network proposed in this paper can deeply analyze the load characteristics through three gate
structures, which is helpful to improve the prediction accuracy. Combined with grey relational degree method, the
prediction speed can be accelerated.
Method:
This paper introduces a grey relational degree method to analyze the factors related to air conditioning load and
selects the best ones. A Long Short Term Memory Neural Network (LSTMNN) prediction model was established. In this
paper, grey relational analysis and LSTMNN are combined to predict the air conditioning load of an office building, and
the predicted results are compared with the real values.
Results:
Compared with Back Propagation Neural Network (BPNN) prediction model and Support Vector Machine
(SVM) prediction model, the simulation results show that this method has better effect on air conditioning load prediction.
Conclusion:
Grey relational degree analysis can extract the main factors from the numerous data, which is more
convenient and quicker without repeated trial and error. LSTMNN prediction model not only considers the relation of air
conditioning load on time series, but also considers the nonlinear relation between load and other factors. This model has
higher prediction accuracy, shorter prediction time and great application potential.
Multifeature pool importance fusion based GBDT (MPIF-GBDT) for short-term electricity load prediction
