The output power of photovoltaic power plants is significantly affected by a variety of external environmental factors, and characterized by nonlinear and large fluctuations. In response to these issues, an ultra short term power prediction method for photovoltaic power generation is proposed, which combines Partial Least Squares (PLS), Genetic Algorithm (GA), and Long Short Term Memory (LSTM). Taking into full consideration of the six environmental factors constraining the PV output power. Firstly, partial least squares regression (Partial Least Squares PLS) is used to extract the key influencing factors of feature sequences. By fully utilizing sequence information, the data size and complexity are reduced, the correlation and redundancy of the original sequence are eliminated, and the dimensionality of the model input is reduced. Then Genetic Algorithm (GA) is used to select the optimal hyperparameters for the LSTM neural network. Ultimately, dynamic time modelling of multivariate feature sequences using LSTM networks is used to achieve the prediction of PV power. The reduction of prediction error of this method compared to single LSTM and CNN models is verified by simulation example analysis and is feasible.
KEYWORDS: Solar energy, Wind energy, Mathematical optimization, Photovoltaics, Information theory, Systems modeling, Instrument modeling, System integration, Modeling, Matrices
Multi energy flow coupling, such as electric heating and cooling, is an important feature of integrated energy system. Aiming at the uncertainty of source load caused by the deviation of wind and solar output prediction and load growth prediction in the optimal configuration of multi energy flow system, an optimal configuration method of electric heating and cooling multi energy flow system considering the uncertainty of source load is proposed. Firstly, the physical mechanism and characteristics of the key distributed energy equipment and energy supply network in the multi flow energy supply network of the energy hub are analyzed; The information gap decision theory is used to deal with the prediction deviation caused by the increase of electric heating and cooling air load in the model, and the conditional risk value theory is used to adjust the modeling scheme to assess the risk loss caused by the wind and solar power output prediction deviation. The example analysis shows the optimal allocation results of the deterministic model and the model taking into account uncertainty. The comparison with other optimal allocation methods verifies the effectiveness and superiority of the method in this paper. At the same time, it analyzes the impact of the changes in the avoidance coefficient and risk coefficient in the model on the model results and investment strategies, which proves that the model has the ability to quantify and evaluate the output uncertainty on both sides of the source load.
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