The invention discloses a microgrid short-term load forecasting method based on independent component analysis and support vector machine, which comprises the following steps: adopting Chebyshevv orthogonal polynomial to carry out curve fitting on historical daily load data of the microgrid region, and adding penalty function to carry out optimization; Updating the historical load data and using independent component analysis method to obtain the load independent separation source and mixed matrix of historical load data; For the meteorological data of historical day and forecast day, principal component analysis is used to obtain the principal component vector, and the decision data is combined with the date data. The mixed matrix of historical load data is used as the target data, combined with the historical day decision data, and the support vector machine (SVM) and fireworks algorithm are used to optimize the penalty factor and the kernel parameter, so as to obtain the optimal SVMtraining model. The forecasted daily load curve is obtained by substituting the forecasted daily decision data. The invention can remove noise interference, and can effectively and accurately predictthe load of the microgrid in a short time in the future.

Short-term load forecasting method for microgrid based on independent component analysis and support vector machine

The invention discloses a structural settlement deformation prediction method based on EMD-SVR-WNN. The method includes following steps: 1) measuring settlement monitoring data of a practical structure, directly processing the monitoring data if an obtained practical monitoring data sample is large enough, and performing interpolation noising and pre-processing on the settlement monitoring data ofthe practical structure obtained through measurement if the monitoring data sample is too small and influences the data prediction precision; 2) performing EMD decomposition on the settlement monitoring data of the practical structure after processing of step 1), regarding a minimum frequency component obtained through EMD decomposition as a trend term of the settlement monitoring data, and regarding a remaining IMF component superposition result as a random term of the settlement monitoring data; 3) performing SVR modeling prediction on the random term of the settlement monitoring data obtained in step 2), and obtaining a random term prediction result; 4) performing WNN modeling prediction on the trend term of the settlement monitoring data obtained in step 2), and obtaining a trend termprediction result; and 5) performing fusion on the random term prediction result and the trend term prediction result, and obtaining the finally predicted structural settlement deformation. Accordingto the method, accurate prediction of the structural settlement deformation can be realized.

Structural settlement deformation prediction method based on EMD-SVR-WNN

The invention discloses a wavelet neural network and support vector machine-based power network weather load prediction method. The method comprises the steps of extracting original data from power data, simplifying the original data by combining a clustering algorithm and a principal component analysis method, performing standardization processing, and inputting the standardized data to a supportvector machine for training to obtain a load prediction model and first predicted load data; inputting the standardized data to a wavelet neural network for training to obtain a trained neural network and second predicted load data; and according to the first and second predicted load data, obtaining the load prediction model. According to the method, the influence of weather data on load fluctuation is fully considered; the data scale is fully considered; through the clustering algorithm and the principal component analysis method, the load and the weather data amount are reduced at the sametime; and the built prediction model ensures the prediction precision of the support vector machine and a wavelet neural network model, improves the prediction precision, and solves the difficult problem of low prediction precision due to consideration of weather factors.

Wavelet neural network and support vector machine-based power network weather load prediction method

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Wavelet neural network and support vector machine-based power network weather load prediction method