A method and apparatus for predicting power consumption, a device, and a readable storage medium. The method includes: acquiring a reference variable generated in a history time period; and acquiring predicted power consumption in a target time period by inputting a variable characteristic into a power consumption prediction model, the target time period and the history time period having a corresponding relationship, and the power consumption prediction model being obtained by training a sample reference variable marked with sample power consumption. In the method, the reference variable including a discrete reference variable and a continuous reference variable in the history time period is acquired, and a characteristic of the acquired reference variable is acquired; and an extracted variable characteristic is input into a variable prediction model to output the predicted power consumption in the target time period.

A method and apparatus for predicting power consumption, a device, and a readable storage medium. The method includes: acquiring a reference variable generated in a history time period; and acquiring predicted power consumption in a target time period by inputting a variable characteristic into a power consumption prediction model, the target time period and the history time period having a corresponding relationship, and the power consumption prediction model being obtained by training a sample reference variable marked with sample power consumption. In the method, the reference variable including a discrete reference variable and a continuous reference variable in the history time period is acquired, and a characteristic of the acquired reference variable is acquired; and an extracted variable characteristic is input into a variable prediction model to output the predicted power consumption in the target time period.

A device for providing charging information for a charging process is configured to determine a total set of N data tuples for N different times of a charging time period for the charging process. A data tuple includes values of one or more characteristic variables relating to electrical energy that can be provided in the charging process. Furthermore, the device is configured to reduce the total set of N data tuples to a reduced set of M data tuples, with M<N, and to provide the reduced set of M data tuples for the determination of a charging plan for the charging process.

A device for providing charging information for a charging process is configured to determine a total set of N data tuples for N different times of a charging time period for the charging process. A data tuple includes values of one or more characteristic variables relating to electrical energy that can be provided in the charging process. Furthermore, the device is configured to reduce the total set of N data tuples to a reduced set of M data tuples, with M<N, and to provide the reduced set of M data tuples for the determination of a charging plan for the charging process.

The present invention discloses a distributed optimization method of regional integrated energy considering different building heating modes, comprising: based on a heating resistance and heat capacity network model, building an RIEDHS optimal scheduling model considering different building heating modes; by a coordination operator, initializing a Lagrange multiplier and global variable information and sending related information to an electricity sub-network and a heating sub-network which perform internal local optimization according to respective sub-problems and return coupling variable information to the coordination operator; and by the coordination operator, receiving the coupling variable information from the electricity sub-network and the heating sub-network, judging whether a convergence condition is met according to the coupling variable information and global variable information: ending the process if so, otherwise updating the Lagrange multiplier and a global variable, and re-executing the local optimization step until the convergence condition is met.

The present invention discloses a distributed optimization method of regional integrated energy considering different building heating modes, comprising: based on a heating resistance and heat capacity network model, building an RIEDHS optimal scheduling model considering different building heating modes; by a coordination operator, initializing a Lagrange multiplier and global variable information and sending related information to an electricity sub-network and a heating sub-network which perform internal local optimization according to respective sub-problems and return coupling variable information to the coordination operator; and by the coordination operator, receiving the coupling variable information from the electricity sub-network and the heating sub-network, judging whether a convergence condition is met according to the coupling variable information and global variable information: ending the process if so, otherwise updating the Lagrange multiplier and a global variable, and re-executing the local optimization step until the convergence condition is met.

Embodiments described herein relate to predicting the utilization of electric vehicle (EV) charge points. Methods may include: receiving an indication of a plurality of candidate locations for EV charge points; determining static map features of the plurality of candidate locations; inputting the plurality of candidate locations and static map features into a machine learning model, where the machine learning model is trained on existing EV charge point locations, existing EV charge point static map features, and existing EV charge point utilization; determining, based on the machine learning model, a predicted utilization of an EV charge point at the plurality of candidate locations; and generating a representation of a map including the plurality of candidate locations, where candidate locations of the plurality of candidate locations are visually distinguished based on a respective predicted utilization of an EV charge point at the candidate locations.

The present invention relates to an aggregation method for dispatching the wind and solar power plants. The primary technical solutions include: introducing the power output complementarity indexes to characterize the average effect of the degree of power output complementarity between different power stations, using cohesive hierarchical clustering to identify the optimal cluster division under different division quantities, and introducing the economic efficiency theory to determine the optimal cluster quantity, which avoids the randomness and irrationality that may result from relying on the subjective determination of the number of clusters. According to the analysis of dozens of real-world wind and solar power cluster engineering in the Yunnan Power Grid, the results show that the invention can effectively reduce the number of directly dispatched power stations, and the uncertainty of wind and solar power output can be more accurately described in a cluster manner, presenting better reliability, concentration, and practicality.

The present invention relates to an aggregation method for dispatching the wind and solar power plants. The primary technical solutions include: introducing the power output complementarity indexes to characterize the average effect of the degree of power output complementarity between different power stations, using cohesive hierarchical clustering to identify the optimal cluster division under different division quantities, and introducing the economic efficiency theory to determine the optimal cluster quantity, which avoids the randomness and irrationality that may result from relying on the subjective determination of the number of clusters. According to the analysis of dozens of real-world wind and solar power cluster engineering in the Yunnan Power Grid, the results show that the invention can effectively reduce the number of directly dispatched power stations, and the uncertainty of wind and solar power output can be more accurately described in a cluster manner, presenting better reliability, concentration, and practicality.

A method for predicting the operation state of a power distribution network based on scene analysis is provided, comprising the following steps of step 10) obtaining the network structure and historical operation information of a power distribution system; step 20) extracting representative scene sequence fragments of output of the DGs according to historical output sequences of the DGs; step 30) obtaining a multi-scene prediction result of a future single-time section T.sub.0 through matching the historical similar scenes; step 40) establishing a future multi-time section operation scene tree; and step 50) deeply traversing all scenes in the future multi-time section operation scene tree, performing power distribution network load flow analysis for each scene, calculating the line current out-of-limit risk and the busbar voltage out-of-limit risk of the power distribution network, and obtaining a future operation state variation tendency of the power distribution network with the DGs.