44results about How to "Reduce forecast bias" patented technology

The invention discloses a building electricity consumption prediction method and system based on Stacking model fusion, and belongs to the field of building electricity consumption prediction. According to the method, multiple regression models are integrated by adopting a Stacking model fusion algorithm, an electricity consumption Stacking integrated model is constructed, the advantages of the multiple models are integrated, and prediction deviation is reduced; for buildings with unstable electricity consumption, the electricity consumption Stacking integrated model is trained by utilizing multiple influence factors such as historical electricity consumption, temperature, wind power, humidity and time information, so that the prediction accuracy is improved, managers of the buildings caneffectively control the energy consumption of the buildings, and the situation that the difference between the electricity consumption and the estimated electricity consumption is too large is avoided; according to the invention, reasonable estimation and purchase are carried out when a building manager participates in electricity market transaction, so that the building manager can effectively control electric charge expenditure, electricity selling arrangement of an electric power department or an electricity selling company is facilitated, the effects of energy conservation and emission reduction can be achieved, and good social benefits and economic benefits are achieved.

The invention provides an energy consumption prediction method and device based on complementary fuzzy neural network. The method comprises steps of: acquiring energy consumption historical data corresponding to energy to be tested; classifying and structuring the energy consumption historical data; performing gray treatment on the structured energy consumption historical data and screening out valid historical data; normalizing the valid historical data; performing fuzzy processing on the normalized data; inputting the data subjected to the fuzzy processing into the fuzzy neural network which predicts an energy consumption predicted value corresponding to the data subjected to the fuzzy processing; performing anti-normalization processing on the energy consumption predicted value; performing whitening processing on an anti-normalization processing result to obtain and output a target predicted value. The energy consumption prediction method and device based on complementary fuzzy neural network may increase the accuracy of the energy consumption prediction results.

The invention provides an air quality prediction method for multi-task learning based on multi-dimensional secondary feature extraction. The air quality prediction method comprises the steps of data acquisition, data preprocessing, pollutant selection, establishment of a convolutional neural network model and a long-term and short-term memory network model for multi-dimensional secondary feature extraction, establishment of a multi-task learning model for multi-dimensional secondary feature extraction and verification. According to the air quality prediction method, the problem that only timeinternal correlation and space internal correlation are considered during traditional spatio-temporal data modeling, and spatio-temporal correlation is not considered is solved. According to the air quality prediction method, influence information related to pollutant values is considered from three perspectives of space, time and space-time, and the prediction deviation is reduced by learning themutual influence among multiple time and space tasks through multi-task learning, so that the prediction precision of time and space models is more accurate.

The method is suitable for the technical field of image / video compression, and provides an image-level JND threshold prediction method and device, equipment and a storage medium. The method comprisesthe following steps: performing perception distortion discrimination on the to-be-detected image and the compressed image in the compressed image set corresponding to the to-be-detected image throughthe trained multi-classification perception distortion discriminator. Obtaining a perception distortion discrimination result set; performing fault-tolerant processing on the perception distortion discrimination result set through an image-level JND search strategy; according to the method, the image-level JND threshold of the to-be-detected image is obtained through prediction, so that the prediction deviation of the image-level JND threshold is reduced, the prediction accuracy of the image-level JND threshold is improved, and the predicted JND threshold is closer to the perception of a humaneye vision system on the quality of the whole image.

The invention provides a load prediction method and device based on an auto-encoder and a meta-learning strategy, and the method comprises the steps: receiving to-be-predicted target time of a targetenergy system; obtaining historical data of the target energy system before the target time, and extracting time sequence characteristics of the historical data by adopting the auto-encoder; selectinga load prediction algorithm matched with the time sequence characteristics from a plurality of algorithms according to a classification model obtained by meta-learning; and outputting the load valueof the target energy system at the target time by using the load prediction algorithm. Through the method and the device, the technical problems of high time consumption and low accuracy in attemptingvarious prediction algorithms when the load prediction algorithm is adopted to predict the energy load in the prior art are solved.

The invention discloses a traffic demand forecasting method and system for a dynamic road network. According to the invention, multi-source traffic data is collected and integrated all the time; dynamic grid data is formed through overlapping traffic flow data, geographic information data and traffic management information on the road network; and a data forecasting model is established on the basis of dynamic road network data. The method has characteristics of good real time performance, high accuracy and the like, can improve jamming study and judge precision effectively, so as to provide reasonable traffic demand data (queue, flow, speed, occupancy rate and the like) for signal optimization. The system can improve an optimization model and relieves traffic jams.

According to the coal reservoir permeability prediction method, surrounding rock stress, gas pressure, temperature and compressive strength serve as input values, permeability serves as an output value, and data are divided into a training set and a test set after being cleaned; a multiple linear regression model, a BP neural network model and an SVM model are established by using the training set, and the test set is predicted; and then a joint prediction model is established, and the most suitable weight is obtained by using a particle swarm algorithm. According to the method, multiple machine learning models are combined, the advantages of all the models are extracted, a more accurate combined prediction model is obtained, prediction deviation is reduced, the combined prediction model has good robustness, and even if deviation occurs due to model assumption, only small influence can be generated on algorithm performance. No matter how the index number and the sample number of the data change, the permeability can be accurately predicted; and the requirement for the training data volume is loose, and a precise prediction model can be obtained through small sample data training.

The invention discloses a method for predicting the bandwidth of intelligent power distribution business data transmission based on queuing theory. Aiming at the problem that the existing bandwidth prediction method is likely to cause a large deviation between the predicted value of the bandwidth and the actual demand value, the method first obtains the bandwidth from The QoS requirement parameters of the intelligent power distribution service at the application layer are used as the basic data for bandwidth prediction, and the high-level QoS parameter indicators are converted into queuing theory model parameters through queuing theory; secondly, the transmission bandwidth prediction based on queuing theory is determined according to the QoS constraints of power communication services At the same time, the prediction model is converted into a linear constraint nonlinear programming solution model to obtain the optimal solution; finally, the minimum prediction transmission bandwidth that can guarantee the system service QoS requirements is obtained. The method obtains the predicted bandwidth through intelligent power distribution service QoS parameter mapping and queuing theory modeling, which can reduce bandwidth prediction deviation and optimize system bandwidth utilization.

The present disclosure discloses a prediction method, device and automatic prediction system for estimating the arrival time of a track, wherein the method includes: obtaining real-time position information of aircraft entering the prediction area; triggering a pre-established prediction model according to the real-time position information of the aircraft The electronic fence in , calculate the first predicted time T that takes time from the current electronic fence where the aircraft is located to the time it takes to receive the signal to align with the runway d ;According to the alignment information of the aircraft to the runway, obtain the second predicted time T from receiving the alignment signal to actually landing. s ; According to the first prediction time T d and the second predicted time T s , and calculate the arrival time of the aircraft based on the sum of the current time. Therefore, by implementing the prediction scheme disclosed in the present disclosure, the prediction deviation caused by unexpected factors can be effectively reduced, the prediction accuracy of the arrival time of airport flights can be improved, the waste of airport security resources can be avoided, and the security risks caused by inaccurate prediction of arrival time can be eliminated. Hidden danger.

The invention provides a method for recommending a target object to a user. The method comprises that: an object feature of the target object is generated according to at least one of an object control feature and an object random feature of the target object; the object control features and the object random features are obtained after multi-task alternate training is performed on a control matching model and a random matching model; in the multi-task alternate training, a control sample is used as an input sample of a control matching model, a random sample is used as an input sample of a random matching model, and an optimization target is achieved by modifying an object control feature of a target object in the control sample or modifying a value of an object random feature of the target object in the random sample, wherein the optimization target comprises that the difference between the object control feature and the object random feature of the same target object is as large aspossible; and the user features and the object features of the target object are input into a matching model, and the target object recommended to the user is determined according to the matching degree of the user features and the object features output by the matching model.