233 results about "Interval prediction" patented technology

The invention discloses a method for accurately estimating wind power prediction error intervals. The method comprises the following steps: firstly, obtaining historical wind power data of a wind power plant; secondly, calculating wind power predication errors of all prediction points of the wind power plant, and establishing a wind power predication error distribution model; thirdly, establishing an error probability density function according to the distribution of the predication errors; fourthly, obtaining a confidence interval, meeting a certain confidence level, of the predication errors according to a given wind power predication value; and fifthly, calculating the shortest confidence interval through a Lagrange multiplier algorithm. On the basis of point predication, a probability density function of wind power prediction errors is obtained through interval prediction, and the confidence interval under a certain confidence level is calculated by a probability theory. In this way, the reliability of the interval to contain a wind power point predication value is determined, and the precision of wind power interval prediction is effectively improved.

The invention provides an efficient routing algorithm (PMSF algorithm) in an opportunistic network. Improvement is performed based on SAW. The transmission performance of a relay node is fully considered in the distribution phase. An improved Prophet delivery prediction function is applied to act as a utility value for allocation of message copies. The success rate of message transmission of the relay node is higher when the transmission prediction value indicated by the delivery prediction function is higher so that more message copies are allocated to the node, and the blind equal distribution mechanism in the classic SAW message distribution phase can be eliminated. Meanwhile, a Direct Delivery passive routing mode of the waiting phase is changed into active routing, and the waiting phase is named as a forwarding phase so as to be better fit with the message multi-hop forwarding mechanism of the active routing phase, and the message is forwarded to the relay node capable of rapidly meeting with a destination node as much as possible by utilizing a Markov time interval prediction model. The principles of efficiency and trustiness are both considered so that the copies can be rapidly distributed and effectively transmitted, and transmission stability and reliability can also be guaranteed.

The invention discloses a method and a device for playing a video and belongs to the field of Internet. The method comprises the steps of receiving a skip request which carries a skip time point, selecting a target image frame satisfying a preset condition of time difference with the skip time point from the key frame of the current video and a plurality of pre-marked interval prediction frames, and playing the current video from the target image frame, wherein every two interface prediction frames are spaced by at least one image frame and the number of the interval frames is smaller than the number of the interval frames of the key frame, and the reference image frame of each interval prediction frame is the key frame or other interval prediction frame. According to the method, the skip accuracy of the video can be improved.

The invention discloses a power consumer load interval prediction method based on deep learning. The method comprises the following steps of (1) establishing a large consumer historical load data preprocessing model; (2) establishing a load point prediction model based on an LSTM time recurrent neural network; and (3) adopting a load interval prediction algorithm of a point prediction value scaling coefficient. In this way, according to the method, a user load preprocessing model based on a state vector machine method is established to carry out preprocessing analysis on the single user historical data; and according to the processed historical data, an LSTM machine learning method is adopted to find a prediction model for reducing a user load prediction error to the maximum extent, and the load interval prediction of a single user is carried out by using a point prediction value scaling coefficient load interval prediction algorithm, so that the accurate load interval prediction can be carried out on the load of the single power user with strong random fluctuation, and the prediction accuracy of the user load is obviously superior to that of a traditional method.

The invention discloses a photovoltaic power interval prediction method combining a deep cycle neural network and parameter estimation, belonging to the technical field of photovoltaic power prediction. The method of photovoltaic power forecasting based on a long-term and short-term memory network firstly chooses the data of product day, ambient temperature, ambient humidity, wind speed and solarirradiance as the original data of photovoltaic power forecasting. The data of product day, ambient temperature, ambient humidity, wind speed and solar irradiance are selected as the original data ofphotovoltaic power forecasting. The confidence intervals of PV power values and predicted values corresponding to 24 hourly hours of the predicted day are outputted from the predicted model to 24 hourly hours of the predicted day for 365 days of the year. This method establishes a relationship between the current photovoltaic power change and the previous photovoltaic power change, realizes the dynamic modeling of the time series data, and can reflect the change law of photovoltaic power more fully, and realizes more accurate photovoltaic power prediction. The method is easy to operate, is high in practicability and has a high promotion value.

The invention belongs to the technical field of short term power load forecasting, and discloses a power load forecasting method based on improved exponential smoothing and a gray model. The method includes the following steps: inputting original power load real-time data, and conducting a single exponential smoothing on the original power load real-time data, weakening the randomness of the original power load real-time data, such that the original power load real-time data approaches exponential development trend; predicting a smoothed sequence by using a gray forecasting model which optimizes background value; conducting inverse exponential smoothing on the forecasting result and returning the result to original power load data and a forecasting value at a next forecasting moment; determining whether the result reaches the requirements of knitting fitting errors, and outputting a forecasting result. According to the invention, the method expands the application range of the gray forecasting model, shortens search intervals, has higher forecasting reliability as high as 97%, can the meet requirements for maintaining the average error of short term power load forecasting at approximately 3% so as to address the problem of short term power load forecasting in future development of intelligent power grids.

The invention discloses a method for predicting a rotating standby interval with wind power acceptance considered based on probability interval prediction. The method for predicting the rotating standby interval with the wind power acceptance considered is characterized in that according to the uncertainty of loads and wind power force output, probability interval prediction is used for replacing point prediction, and then a probability prediction theory and prediction error probability distribution are used for predicting a load variation interval and a wind power force output variation interval; then according to a limitation scene theory, the load prediction interval and the wind power force output variation interval are used for obtaining the rotating standby interval with a large-scale wind power system, so that a positive standby value and a negative standby value needed by the system under a limitation condition are given out, and a reasonable data range is provided for power coordinated optimization dispatching with the wind power acceptance considered.

There is provided an image forming apparatus comprising: detection means for detecting position information eating a scanning position; interval prediction means for predicting a first scanning line interval indicating a distance in the sub-scanning direction between the scanning line of interest and a succeeding scanning line to be scanned after the scanning line of interest; interval calculation means for calculating, by using the position information held by the holding means, a second scanning line interval indicating a distance in the sub-scanning direction between the scanning line of interest and the scanned scanning line; and rate calculation means for calculating a correction rate on an exposure amount for the scanning line of interest so that a predicted density calculated using the first scanning line interval and the second scanning line interval matches with a predicted density calculated using a predetermined reference scanning line interval.

The invention provides a prediction method and system for the confidence interval of software cost. The prediction method comprises the following steps of: acquiring process data and cost information of a known software project from a project management database; specifying which columns the respective process data and cost information are arranged to form a data matrix X through configuration items after data preprocessing on the basis that each row is the process data of each item of software, wherein the cost information forms a column vector Y; extracting data from an X-Y matrix, and inputting the data in a core modular module for training; training out parameters of a selected model according to input data of the X-Y matrix, and figuring out a measured value of the cost; and obtaining a final prediction interval by using the measured value of the cost and the known cost value according to a calculation method of the confidence interval. In the invention, the interval prediction method has a high hit rate of experiments, and plays an extremely important and dependable instruction role in the measurement of workload in practical application; when the prediction system is adopted, a new model, a new interval algorithm and a new model evaluation method are added for researchers; and therefore the prediction method and system are very convenient.

The invention discloses a wind power short-term interval prediction method based on an RT reconstructed EEMD-RVM combined model. Firstly, ensemble empirical mode decomposition is conducted on an original wind power sequence to obtain a stable intrinsic mode function (IMF) component and a remaining (RES) component with different features; by means of the runs-test method (RT), fluctuation degree detection is conducted on the components, and the similar components are reconstructed into three new components, with typical features, including a random component, a detail component and a trend component according to the fine-to-coarse sequence; then, a relevance vector machine (RVM) is adopted to the new components to build interval prediction models respectively; finally, prediction results of the new components are superposed to obtain a total interval prediction result under a certain confidence level. By means of the method, prediction precision of the models and the interval coverage are improved, the interval width is obviously reduced, and accordingly the prediction result is remarkably improved.

The invention provides a photovoltaic power generation power seasonal multi-model interval prediction method based on an extreme learning machine and nuclear density estimation, and the method comprises the steps: firstly, analyzing the output power, power deviation, power change rate and other indexes of a photovoltaic power station, and indicating that the photovoltaic power output and fluctuation show obvious seasonal distribution characteristics through a result; establishing a deterministic prediction model of photovoltaic output in different seasons through the neural network of the extreme learning machine; secondly, fitting error distribution of deterministic prediction through a non-parameter kernel density estimation method, and then obtaining a photovoltaic power prediction interval meeting a certain confidence level. According to the method, possible fluctuation ranges of photovoltaic power under different confidence levels can be described, an approach for evaluating the reliability of a prediction interval is provided, and support is provided for risk evaluation and system reliability analysis of the photovoltaic power station.

The invention discloses an inter-bus-station operation time interval prediction method based on a support vector machine. Firstly data cleaning is performed on bus GPS original data; then bus arrivaltime is extracted from the data and the inter-bus-station operation time of the bus is calculated; the relevant information is selected to establish an inter-bus-station operation time interval prediction model input data set; two support vector regression machines are established to predict the upper and lower bounds of the bus operation time; parameter optimization is performed on the support vector machines by using a particle swarm algorithm, and the higher effective coverage of the prediction interval and the lower average width of the standard prediction interval act as the parameter optimization objectives; and the final inter-bus-station operation time interval prediction model is constructed according to the optimal parameters obtained by the PSO algorithm. Real-time and accuratebus arrival time interval prediction can be provided for the travelers under the uncertain situation so that planning and selection of the traveling route can be facilitated for the travelers.

The invention provides a wind power cluster power interval prediction method and system based on deep learning. The method comprises steps that numerical weather forecast and historical wind power ofeach wind power station are obtained as original input data; mutual information between an interpretation variable and a target variable in a region is extracted by calculating the mutual informationof the interpretation variable so as to extract associated information; interpretation variables conforming to relevancy are selected; data reconstruction and dimension reduction are carried out by using a principal component analysis method. According to the method, the interval constraint condition is constructed, the prediction model is constructed by using deep learning, the reconstructed anddimensionality-reduced data is input into the model to be trained, model optimization is carried out by combining a particle swarm optimization method, the final prediction model is determined, and power interval prediction is carried out by using the final prediction model, so that the method has relatively high accuracy.

A prediction device for predicting a prediction interval being a time interval predicted with a high possibility of appliance utilization starts includes: an evaluation unit calculating, using utilization interval history data, an evaluation value for determining a prediction scheme for each appliance; a determination unit, based on the evaluation value at least for each appliance, determining at least one of a first and a second prediction scheme as the scheme of the prediction interval for the appliance, the first prediction scheme using a property that the appliance is utilized in a predetermined cycle, and the second prediction scheme using a property that the utilization of a second appliance being the appliance is started in a period from a start or an end of utilization of a first appliance being another appliance to a passage of a predetermined time; a prediction unit predicting a prediction interval according to the determined scheme.

The invention discloses a method for predicating interval probability of short-term wind power. The method comprises the following steps: acquiring a number of historical wind power from a wind power plant as a sample set; establishing optimization criteria according to the prediction interval coverage probability, the prediction interval bandwidth mean square root and the prediction interval average offset; establishing the interval predicating model of the short-term wind power based on an artificial bee colony nerve network, optimizing and updating a nerve network weight threshold to the optimization criteria through an artificial bee colony algorithm; according to the optimal weight threshold, establishing a nerve network and performing interval predication to the wind power to be predicated; performing state division to the historical wind power, establishing a Markov chain prediction model, and calculating the transition probability of each status; predicating the wind power interval according to the Markov chain status transition probability and the interval predication, and calculating the probability of the numerical point in the predication interval. When the short-term wind power interval predication is executed, the probability distribution of the numerical point in the interval is considered, thus the method can provide the basis to an optimization system.

The invention discloses a method and system for predicting a degradation trend interval of a hydroelectric generating set. The method comprises the following steps: based on the historical state monitoring data of the hydroelectric generating set, establishing a set health model by using power and waterhead and set vibration data in fault-free healthy state in a previous period, obtaining a set degradation trend model by using the power and waterhead data in a faulty state in a later period, and finally establishing a fuzzy interval prediction model of the degradation trend of the hydroelectric generating set. Further, structural division is performed on the model, and model parameter optimization is performed by using the coverage of a fuzzy model interval prediction result and a comprehensive index of an interval width. By adoption of the method and system disclosed by the invention, the identification precision of the fuzzy interval prediction model of the degradation trend of the hydroelectric generating set is improved, more accurate identification parameters can be obtained, and the prediction process is simplified.

The invention discloses a nuclear extreme learning machine quantile regression-based wind power interval prediction method. The method comprises the steps that a wind power plant output power and windspeed data is collected; the data is processed simply, and unreasonable data is deleted; a nuclear extreme learning machine quantile regression model is built; by means of a particle swarm algorithm,nuclear extreme learning machine quantile regression parameters are optimized, and a regression module is determined; test data is put, and a wind power prediction interval is obtained. Accordingly,the nuclear extreme learning machine quantile regression principle and a nuclear extreme learning machine model are effectively combined, and the optimal model parameter is obtained by conducting search and optimization through the particle swarm algorithm, uncertain information in wind power can be effectively grasped, then a better prediction result is obtained, and the basis can be provided forsafe and stable running of wind power integration.

The invention discloses a genetic support vector machine photovoltaic power interval prediction method based on quantile regression. The method comprises the steps of extracting a solar radiation value, a temperature value and photovoltaic power of historical data to obtain a data sample, and performing normalization preprocessing; optimizing parameters of a support vector machine through a genetic algorithm to overcome the fluctuation and randomness of photovoltaic power generation, building a prediction model, and obtaining high-precision photovoltaic deterministic predictive power; and by analyzing a photovoltaic power prediction error of the prediction model, determining a quantile regression variable, and building a corresponding quantile regression model according to uncertain weather factors, so that photovoltaic power interval prediction is achieved. According to the method, photovoltaic power prediction error distribution does not need to be assumed; accurate photovoltaic power interval prediction ranges under different confidence degrees are obtained; richer information is provided for dispatching decision and operation risk assessment of an electric power system; and thetechnical problem of a non-ideal photovoltaic power interval prediction result is solved.

A heat load area prediction method based on chaos theory is provided, which relats to a prediction method of heat load. The invention solves the problems that the prediction method of the heat load in prior art depends on a plurality of physical data and weather forecast information, point prediction does not satisfy the requirement of heating power schedule on reliability of load forecast, and the heat load area prediction method is lack of inherent regularity description. The method comprises: 1. state space reconstitution of heat load time sequence; 2. chaos recognition of the largest Lyapunov exponent; 3. span forecast of the largest Lyapunov exponent. The invention is directly applied on heat supply energy saving reconstruction, heating power schedule and heating power station control.

The invention belongs to the technical field of runoff prediction, and discloses a runoff probability prediction method and system based on deep learning, and the method comprises the steps: employinga maximum information coefficient to analyze the linear and nonlinear correlation between variables, so as to screen a runoff correlation factor; building an extreme gradient boosting tree model on the basis of correlation analysis, and inputting runoff correlation factors into a trained XGB model to complete runoff point prediction; inputting a point prediction result obtained by the XGB model into a GPR model, and performing secondary prediction to obtain a runoff probability prediction result; selecting confidence and acquiring a runoff interval prediction result under the corresponding confidence through Gaussian distribution; and optimizing hyper-parameters in the XGB model and the GPR model by adopting a Bayesian optimization algorithm. A high-precision runoff point prediction result, an appropriate runoff prediction interval and reliable runoff probability prediction distribution can be obtained, and the prediction method plays a crucial role in utilization of water resourcesand reservoir scheduling.

The invention discloses a wind power interval prediction method based on kernel density estimation and an implementation system thereof, and the method comprises the steps: firstly carrying out the deterministic point prediction of wind power based on a support vector machine modeling method of continuous time period clustering, and obtaining a comparison graph of a wind power prediction value andan actual value; secondly, establishing a probability density function for the prediction error in each power partition by adopting kernel density estimation and optimal window width selection, and performing comparative analysis with a probability density curve and an error frequency histogram obtained by a recursion method and a sliding window width method; and finally, calculating a wind powerprediction interval meeting a certain confidence probability in combination with a deterministic point prediction result, and performing comparative analysis with a recursion method and a sliding window width method by taking an interval coverage rate and an interval average width as evaluation indexes. According to the scheme, the random change rule of the wind power error is reflected more accurately, the obtained interval prediction effect is better, the precision is higher, and a more accurate wind turbine generator output interval range is provided for making an economic dispatching planfor a power system.

The invention discloses a product performance degradation interval prediction method based on support vector machine (SVM) and fuzzy information granulation, comprising the following steps of: step 1, collecting product multi-parameter performance degradation data; step 2, analyzing principal components of the multi-parameter degradation data; step 3, executing fuzzy information granulation on the obtained principal component data; step 4, executing SVM modeling for the granulated data; step 5, executing interval prediction on product performance degradation trends. In the method, a fuzzy information granulation method and a SVM method are combined, the interval prediction method for the product performance degradation trends is presented for the first time, and the problem of prediction on the degradation trends of performance state and the changing space in the product running process is solved. The method solves the problem of evaluation and prediction under a condition that a plurality of output performance characteristic parameters of some products with complex structures concurrently degrade, based on the principal component analysis method.

The invention discloses a wind power interval prediction method based on atom decomposition and interactive fuzzy satisfaction. According to the method, an improved atom sparse decomposition method isused to decompose an original wind power sequence into a series of subsequences; each subsequence is subjected to sample entropy calculation, and then the subsequences are recombined into a random component, a cyclic component and a trend component according to sample entropy of each subsequence; a prediction model is established through a core extreme learning machine for each component, and meanwhile a wind power interval objective function based on interactive fuzzy satisfaction is established considering coverage, average bandwidth and bandwidth deviation of a wind power interval; and a teaching and learning algorithm is used to perform optimization, and an optimal wind power probability interval is obtained through prediction. Through the method, the problem that a traditional wind power prediction method cannot reflect uncertainty of a prediction result is solved.

The invention belongs to the technical field of information, and provides a wind power output short-term interval prediction method. According to the invention, the method comprises the steps: employing industrial real data, constructing a multi-level information granularity non-equal-length distribution structure, and establishing a corresponding optimization model; furthermore, considering the importance of the model structure to the prediction precision, and carrying out reinforcement learning on the structure parameters of the multi-level model through the Monte Carlo method; and finally,based on the optimal multi-layer granularity calculation structure, acquiring a long-term interval prediction result of the coal gas production and dissipation amount by applying a parallel calculation strategy. The result obtained through the method is high in precision, the calculation efficiency meets the actual application requirement, and the method can also be applied and popularized in other energy medium systems in the iron and steel industry.

The invention discloses a satellite power source main bus-bar current interval prediction method. According to the method, based on a prediction model trained by an optimized kernel extreme learning machine, a prediction interval is determined by using a proportionality coefficient method, and the parameters of the proportionality coefficient method are optimized by using a differential evolution algorithm. The method specifically includes the following steps that: satellite power source main bus-bar bus current data are preprocessed, noise data can be removed, and normalized data can be obtained; the parameters of the kernel extreme learning machine are optimized by adopting the differential evolution algorithm; the optimized kernel extreme learning machine is adopt to construct an initial prediction model; comprehensive indexes for evaluating the quality of the prediction interval are given, the prediction interval is determined through adopting the proportionality coefficient method, and the satisfaction degree of the prediction interval is evaluated; and the prediction proportionality coefficient of the interval is optimized through using the differential evolution algorithm, so that an optimal satellite power source main bus-bar current prediction interval can be obtained. The satellite power source main bus-bar current interval prediction method of the invention is based on complicate satellite power source main bus-bar current data, and has the advantages of higher prediction accuracy and better effect.

The invention discloses a hydropower station load interval prediction method. The historical load data is fully utilized, a similar day set of the day to be predicted is obtained by calculating the linear difference between the known moment load sequence of the day to be predicted and the actual load sequence of each historical day, the point predication is carried out on the day to-be-predicted according to the similar day of the day to be predicted, and then a large amount of historical load prediction error samples are analyzed to obtain a probability interval result of the possible valuesof the future loads. According to the interval prediction result, a hydropower station decision-making person can better recognize the uncertainty and the risk factors possibly existing in the futureloads during the production plan and the real-time scheduling, so that a more reasonable decision is made in time, and a basis is provided for the real-time load distribution of the hydropower station.

The invention belongs to the technical field of information, relates to a resampling method, a Bootstrap estimation and Bayesian estimation method and an echo state network integration theory, and specifically relates to a metallurgy enterprise gas flow interval predicting method based on Bootstrap echo state network integration. The method comprises the steps of firstly, performing re-sampling processing on the flow data of each user of a gas system to construct an effective training sample by use of the existing historical data of a metallurgy enterprise site, secondly, establishing an interval prediction model based on the echo state network integration and predicting the gas system user flow within specified time length after a current time point, and finally, estimating the influence of the uncertainty of the model and the data on the prediction result based on the Bootstrap method and the Bayesian method, respectively, thereby constructing a confidence interval and a prediction interval. The metallurgy enterprise gas flow interval predicting method based on Bootstrap echo state network integration can be widely applied to other energy medium systems of the metallurgy enterprises.

Provided is an interval prediction control modeling and optimizing method based on soft constraints. The control method comprises the following steps: (1) a quadratic performance index including a constraint item, a control item and an economic item is established based on a process prediction model; (2) whether an overall optimization method is feasible is judged by solving a slack variable; (3) a method for solving a soft constraint slack variable when a control model output constraint is not feasible is provided, and adjustment of the range of a feasible region when an interval prediction control model output constraint is not feasible is realized; and (4) a boundary feasible sequence quadratic programming method is adopted to solve the problem that poor initial point selection causes calculation amount increase of the method or difficulty in finding an optimal solution, the problem that the positive definiteness of a Hessian matrix is destroyed due to the influence of round-off error in calculation, and the like, and to figure out the optimal control input. A complicated multivariable system control model can be established, the control law can be solved accurately and quickly based on soft constraint adjustment, and good control on a multivariable system can be achieved.

The invention discloses a wind power non-parametric interval prediction method based on self-adaptive double-layer optimization, and belongs to the field of renewable energy probability prediction. According to the method, an extreme learning machine and quantile regression are combined to carry out modeling on a prediction interval to form a lower-layer optimization problem; and the quantile level corresponding to the prediction interval is adaptively adjusted by taking the interval sharpness as a target to form an upper-layer optimization problem. Efficient and reliable training of the prediction model is realized by using a primal-dual interior point algorithm. The method does not need to depend on the priori hypothesis of wind power probability distribution, breaks through the centralsymmetry limitation of the traditional probability prediction on the interval quantile level, remarkably improves the reliability and sharpness of the prediction interval, and provides important reference for the operation and control of a high-proportion wind power system.