The invention discloses an ARIMA (Autoregressive integrated moving average) model load prediction based parallelization computing method. The method comprises the following steps: partitioning original electrical load data to form time series data according to the electrical load characteristic and strong relevance of data at the same moments every day; newly constructing a plurality of threads, synchronizing all threads, setting a counter shared globally, executing run () function to parallelize processed and partitioned data by use of the newly constructed thread, judging whether the counter in run () is locked, if so, queuing up; if not, locking the counter in the run (), and counting by adding 1; predicting by calling ARIMA according to data in the time series data, corresponding to the numerical value of the counter; and acquiring the predicted data, unlocking, repeating the steps until data in the time sequence data is completely processed. The method can remarkably improve the computing efficiency.

ARIMA (Autoregressive integrated moving average) model load prediction based parallelization computing method

The invention relates to a cloud base height inversion method based on a foundation cloud picture. The cloud base height inversion method comprises the following steps: (1) selecting places, deployingtwo whole-sky imaging instruments, setting a field angle of the two whole-sky imaging instruments to be not greater than 130 degrees, enabling the two whole-sky imaging instruments to perform synchronous observation, and enabling the observed foundation cloud picture to have an overlapped area; (2) acquiring the foundation cloud picture synchronously observed by the two all-sky imaging instruments, performing the image matching for the two foundation cloud pictures, and selecting the overlapped area with the smallest difference; and (3) for a cloud cluster selected in the overlapped area in amatching manner, and a cloud base height is inversed by utilizing a geometric relation to obtain an inversion result. Compared with the satellite cloud picture observed from the cloud top in the existing inversion method, the foundation cloud picture is observed on the ground by the whole-sky imaging instruments, i.e., the foundation cloud picture is observed from the cloud base, so that the accuracy of the cloud base height inversion result is improved, and the effect is more significant for the thick cloud and the low cloud. In addition, the application range of the whole-sky imaging instrument is also expanded.

Cloud base height inversion method based on foundation cloud picture

The invention discloses a wind power plant full-field power predication method. The method includes the following steps: wind power plants judge whether weather forecast information can be obtained, and if a judgment result shows that the weather forecast information can be obtained, the wind power plants conduct power prediction or combine the weather forecast information with the wind speed predicated by the wind power plants to conduct power predication on the basis of the weather forecast information; and if the judgment result shows that the weather forecast information cannot be obtained, the wind power plants train a wind speed predication model through historical data, a predicated wind speed is obtained through calculation by the wind speed predication model, power predication is performed on the basis of the predicated wind speed, a predication result is sent to a power grid scheduling center, and the power grid scheduling center makes subsequent grid connection operation plans on the basis of the predicated power and arranges maintenance operations to be conducted in a time period/region with low electric quantity capacity. According to the method, operations can be carried out without weather forecast data, so the adaptability of a system is greatly improved, and the prediction precision can be further improved in the presence of meteorological conditions.

Wind power plant full-field power predication method

The embodiment of the invention discloses a high-speed train running speed control method and device, a storage medium and a high-speed train. The method comprises acquiring real-time air speed data at an air speed detecting position as model input data, and inputting the model input data into an air speed prediction model which is a statistical model obtained by training of an initial statisticalmodel according to the historical air speed data at the air speed detecting position, wherein the initial statistical model comprises an AR model, an MA model and an ARMA model, acquiring an air speed prediction result according to the air speed prediction model, determining a predicted air speed when the high-speed train runs to the air speed detecting position according to the air speed prediction result, and providing running speed control information to the high-speed train according to the predicted air speed. The technical scheme provided by the embodiment realizes control of a train running speed according to an air speed and improves the safety of the high-speed train in running.

High-speed train running speed control method and device, storage medium and high-speed train

The invention discloses a grey correlation time sequence based short-term wind speed forecasting method, which comprises the steps of (10) forming historical wind speed time sequence data, wherein actually measured wind speeds of a wind power plant are arranged according to a time sequence so as to form a historical wind speed time sequence; (20) acquiring a training sample set, wherein the historical wind speed time sequence is differentiated so as to acquire the training sample set; (30) acquiring a grey correlation optimization wind speed forecasting model, wherein optimized decision-making analysis under multiple targets is carried out on the order number of a time sequence model by applying a grey correlation decision-making analysis method, and grey time sequence model training is carried out by applying the training sample set so as to acquire an optimal wind speed forecasting model; (40) acquiring the differentiated short-term forecast wind speed, wherein short-term wind speed forecasting is carried out on the wind power plant by using the optimal wind speed forecasting model so as to acquire the differentiated short-term forecast wind speed; and (50) acquiring the actual short-term forecast wind speed, wherein inverse differentiation is carried out on the short-term forecast wind speed so as to acquire the short-term forecast wind speed of the wind power plant. The short-term wind speed forecasting method disclosed by the invention is small in forecast error.

Grey correlation time sequence based short-term wind speed forecasting method

The invention provides an ultra-short period wind power prediction method based on a wind speed factor control model. The ultra-short period wind power prediction method includes performing an ultra-short period prediction according to an ARIMA (autoregressive integrated moving average) model; performing the ultra-short period power prediction according to a BP (back propagation) neutral network model; completing the ultra-short period wind power prediction based on the wind speed factor control model. In this way, at the premise that the ARIMA model and the BP neutral network model are evaluated, the ARIMA model and the BP neutral network model can be switched according to the running environment, so that accuracy of the ultra-short wind power prediction is improved.

Ultra-short period wind power prediction method based on wind speed factor control model

The invention provides a cloud cluster height measuring method based on a dual foundation cloud atlas. The method comprises the following steps: step A, determining the relative distance and relative direction between two imagers; step B, recognizing the identical cloud clusters in the cloud atlases of two imagers and extracting the position information of the identical cloud cluster; step C, determining the height of the cloud cluster based on the position information. According to the measuring method, the observation images of dual foundation cloud imager are used, the same cloud clusters are recognized, the position angles are extracted, and thus the height of the bottom of the cloud clusters above a region can be accurately calculated. Compared with the conventional ceilometer, which can only measure the heights of bottoms of clouds that are right above the ceilometer, the provided method can measure the height of clouds that are right above the imagers and is capable of measuring the height of clouds in other positions in the observation range of the imagers; and the method has the advantages of wide coverage and high calculation precision.

Cloud cluster height measuring method based on dual foundation cloud atlas

The invention provides a terrestrial radiation calculation method based on all-sky image features. The terrestrial radiation calculation method comprises the following steps of: (1), obtaining the all-sky image features; (2), calculating the radiation attenuation rate gamma of exoatmosphere solar radiation reaching the ground according to historical data of an exoatmosphere solar radiation value I0 and a terrestrial radiation value Ig; (3), constructing a multiple regression model of the all-sky image features and the radiation attenuation rate gamma; and (4), obtaining a predicted terrestrial radiation value I'g according to the multiple regression model and the exoatmosphere solar radiation value I0. Compared with the prior art, the terrestrial radiation calculation method based on the all-sky image features provided by the invention has the advantages that: the multiple regression model of the radiation attenuation rate and the all-sky image features is constructed according to the historical data of the radiation attenuation rate and the all-sky image features; the optimal solution coefficient of the model is calculated by using a least square method; therefore, it is unnecessary to configure parameters in engineering application; and, on the premise that the exoatmosphere solar radiation value I0 (t) and the predicted radiation attenuation rate value gamma' (t) at t time are known, the predicted terrestrial radiation value I'g (t) at t time is accurately obtained.

Terrestrial radiation calculation method based on all-sky image features

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Ultra-short period wind power prediction method based on wind speed factor control model

Cloud cluster height measuring method based on dual foundation cloud atlas

Terrestrial radiation calculation method based on all-sky image features