The invention discloses a fused extreme learning machine-based prediction method and system of future temperature of a battery. The method includes training steps and prediction steps. The system includes a training unit and a prediction unit. According to the method and system, high-precision prediction of the future temperature of the battery can be realized at low computational costs through the training steps and the prediction steps, and dependence on battery models can be greatly reduced in a prediction process; and a future temperature change situation of the battery can be effectively predicted, and a computational amount is greatly reduced, and does not exceed 10% of a computational amount of a traditional neural network. The method and system can be widely applied to battery temperature prediction.

Fused extreme learning machine-based prediction method and system of future temperature of battery

The invention discloses a wind speed prediction method and prediction system. The prediction method comprises the following steps: acquiring an original wind speed sequence; performing empirical modedecomposition on the wind speed sequence, and obtaining a plurality of inherent modal functions and residual items; classifying all inherent modal functions according to the instantaneous frequency mean value of each inherent modal function, obtaining a plurality of high-frequency modal functions and a plurality of low-frequency modal functions; training the least square support vector machine byadopting the training sample data of each high-frequency modal function to obtain a high-frequency prediction model; training a BP neural network through the training sample data of each low-frequencymode function to obtain a low-frequency prediction model; training the BP neural network through the training sample data of the residual items to obtain a residual prediction model; predicting the wind speed by utilizing all the high-frequency prediction models, the low-frequency prediction models and the residual prediction models. A prediction model is built on the basis of fluctuation characteristics of different components, the random fluctuation of the wind speed sequence can be effectively weakened, and the wind speed can be accurately predicted.

Wind speed prediction method and wind speed prediction system

The invention relates to a prediction method for photovoltaic power. The prediction method comprises the following steps that: carrying out wavelet decomposition on a real-measurement historical photovoltaic power sequence, and obtaining the low frequency component and the high frequency component of the historical photovoltaic power, wherein the low frequency component and the high frequency component have different energy values; taking the strong correlation variable, which is obtained in advance, of the historical photovoltaic power as the input of a classification model, independently taking the low frequency component and the high frequency component as the output of the classification model, and establishing a mapping relationship between the strong correlation variable and the low frequency component and the high frequency component; obtaining the strong correlation variable related to the photovoltaic power to be predicted, and obtaining the low frequency component and the high frequency component of the photovoltaic power to be predicted according to the strong correlation variable related to the photovoltaic power to be predicted and the mapping relationship; and carrying out the wavelet decomposition on the low frequency component and the high frequency component of the photovoltaic power to be predicted to obtain a photovoltaic power prediction value. The invention also relates to a prediction device. By use of the prediction method and the prediction device, the photovoltaic power can be accurately predicted.

Prediction method and prediction device for photovoltaic power

The invention provides a photovoltaic output power prediction method and system and a storage medium. The method comprises the steps of obtaining photovoltaic output data and meteorological data in apreset historical time period; decomposing the photovoltaic output power sequence by adopting a wavelet packet decomposition algorithm to obtain 2n decomposition sequences, and respectively carryingout single-branch reconstruction on the 2n decomposition sequences to obtain 2n reconstruction sequences; combining the 2n reconstruction sequences with the corresponding meteorological data to obtain2n data sets; respectively inputting the 2n data sets into corresponding pre-trained long-short-term memory neural network units to obtain prediction results of the 2n long-short-term memory neural network units on photovoltaic output power in a preset future time period; and performing weighted summation on the prediction results output by the 2n long-short-term memory neural network units to obtain a comprehensive prediction result of the photovoltaic output power in the preset future time period. The prediction precision can be improved.

Photovoltaic output power prediction method and system and storage medium

The invention relates to a mixed wind speed prediction method based on wavelet decomposition considering component correlation Original data is divided into a training set and a testing set The training set is decomposed into a plurality of subsequences According to the correlation coefficient of the subsequences and the original data, false subsequences are identified, and are eliminated correspondingly A prediction model is established for the residual subsequences, and the first to the nth data of the subsequences are used to predict the (n+1)th data The (n+1)th data 1 of the various subsequences are superposed with each other to acquire a final prediction result The training set is updated, and the updated training set is decomposed into a plurality of new subsequences An LSSVM model is established to predict the (n+2)th data, and the (n+2)th data of the various subsequences are superposed with each other to acquire a predicted value The one-step-ahead prediction is continued until all of the predictions are completed The influences of the false parts of the subsequences are reduced, and the performance, the accuracy, and the stability of the prediction are improved

Mixed wind speed prediction method based on wavelet decomposition considering component correlation

The invention relates to the technical field of wind/photovoltaic power generation, and discloses a wavelet packet-neural network-based wind/photovoltaic power prediction method The method comprises the following steps of: firstly analyzing weather forecast data and history data, and assessing the influences, on the photovoltaic power prediction accuracy, of the weather forecast data and the history data; analyzing the influences, on the wind power prediction accuracy, of factors such as temperature, humidity and atmospheric pressure in the natural environment; carrying out wavelet packet decomposition on the data to obtain different frequency component sequences of a meteorological factor sequence and of a wind/photovoltaic active power sequence to ensure that the representation of change characteristics on various spatial and temporal scales is more accurate, wherein the low-frequency components and the high-frequency components respectively map the interior change rules of predicted objects from two scales of profile and detail; aiming at each sequence component, establishing a plurality of BP networks to carry out prediction; and finally reconstructing each network prediction output to obtain a complete prediction result The method disclosed by the invention has the effects of improving the ability of mapping wind/photovoltaic power change characteristics and effectively improving the wind/photovoltaic power prediction accuracy

Wavelet packet-neural network-based wind/photovoltaic power prediction method

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Wavelet packet-neural network-based wind/photovoltaic power prediction method