A dynamic load forecasting and scheduling optimization method, system, device and storage medium

Abstract

The application discloses a dynamic load prediction and scheduling optimization method, system, device and storage medium, which comprises the following steps: collecting demand side multi-source influence factor data and preprocessing; constructing a daily characteristic vector based on the preprocessed data, calculating the multi-factor comprehensive similarity of the prediction day and each historical day, and screening the historical similar days; performing multi-scale decomposition on the load sequence of the historical similar days, constructing a fuzzy neural network prediction model, optimizing the network parameters of the model, and generating a load prediction result; setting an optimization target based on the load prediction result, implementing different demand side response strategies for different users, solving a multi-objective optimization model, generating an optimal scheme, and delivering the optimal scheme to a power load management terminal for execution; and when it is detected that the deviation between the actual load and the prediction value exceeds a preset threshold or the equipment is abnormal, an emergency optimization process is automatically triggered. The application can realize accurate load prediction and optimal allocation of power resources, and improve the safety, economy and reliability of the power system.

Description

Technical Field

[0001] This invention relates to the technical field of power system dispatching and energy management, and in particular to a dynamic load forecasting and dispatching optimization method, system, device and storage medium. Background Technology

[0002] Smart grids rely on advanced information, communication, and automation technologies to achieve intelligent management of power systems. Demand-side response (DR) technology, as a core component of smart grids, guides users to adjust their electricity consumption behavior to achieve dynamic balance between power supply and demand, playing a crucial role in improving the flexibility and stability of the power system. Demand-side response (DR) technology refers to a technical system that guides users to change their electricity consumption behavior in response to changes in power system supply and demand through price signals, incentives, or direct control. It is mainly divided into two categories: Price-based demand-side response: using real-time electricity prices, peak-valley prices, and other price signals, it guides users to increase electricity consumption during off-peak hours and decrease it during peak hours, achieving autonomous adjustment of electricity consumption behavior; Incentive-based demand-side response: using economic compensation, rewards, and other incentive measures, it encourages users to reduce load during peak hours or increase load during off-peak hours, actively participating in the balance of power supply and demand. Demand-side response technology can effectively reduce the peak-valley difference in the power system, improve operational efficiency and reliability, while promoting the consumption of renewable energy and reducing dependence on traditional fossil fuels, thus achieving both economic and environmental benefits.

[0003] Load forecasting is fundamental to power system operation and planning. Traditional methods, including time series analysis, regression analysis, and exponential smoothing, while meeting basic forecasting needs, suffer from poor adaptability to complex and variable power loads (such as load fluctuations caused by extreme weather or unforeseen events), resulting in unsatisfactory forecasting accuracy. With the development of artificial intelligence, modern methods such as neural networks, support vector machines, and deep learning are increasingly being applied to load forecasting. For example, Long Short-Term Memory (LSTM) networks can effectively process time series data and improve the accuracy of short-term load forecasting, but existing methods still have shortcomings: insufficient robustness to extreme scenarios and inadequate integration of demand-side response-related influencing factors (such as electricity prices and user behavior preferences), leading to deviations between forecast results and actual demand-side electricity consumption characteristics. Dispatch optimization is crucial for achieving economical and safe operation of the power system. Traditional dispatch optimization methods are based on mathematical programming algorithms such as linear programming, integer programming, and dynamic programming, aiming to minimize generation costs and maximize system reliability. However, with the expansion of power system scale and the integration of distributed energy resources, traditional methods face problems such as high computational complexity, long solution times, and weak multi-objective collaborative optimization capabilities. In recent years, intelligent optimization algorithms such as genetic algorithms, particle swarm optimization, and simulated annealing have been widely used in the field of scheduling optimization, while multi-objective optimization theory has provided new directions for scheduling decision-making. However, existing scheduling optimization technologies suffer from a core problem of insufficient synergy: they have not formed an integrated solution for demand-side response, load forecasting, and scheduling optimization, making it difficult to adjust scheduling strategies in real time based on dynamic load forecasting results, resulting in low efficiency in power resource allocation. Summary of the Invention

[0004] In view of the aforementioned existing problems, this invention is proposed. Therefore, this invention provides a dynamic load forecasting and scheduling optimization method, system, device, and storage medium to solve the problems of low load forecasting accuracy, poor coordination between scheduling optimization and demand-side response, and insufficient power system operating efficiency in the prior art.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, embodiments of the present invention provide a dynamic load forecasting and scheduling optimization method, comprising: Collect demand-side multi-source impact factor data and preprocess the multi-source impact factor data; Based on the preprocessed data, a daily feature vector is constructed, and a multi-factor comprehensive similarity calculation is performed between the predicted day and each historical day. Based on the calculation results, historically similar days are selected. The load sequences of the historically similar days are decomposed at multiple scales, and a fuzzy neural network prediction model is constructed by combining meteorological factors. The network parameters are then optimized using an adaptive genetic algorithm to generate load prediction results. Based on the load forecast results, optimization objectives are set, different demand-side response strategies are implemented for different users, an improved genetic algorithm is used to solve the multi-objective optimization model, the optimal solution is generated and sent to the power load management terminal for execution; When the deviation between the actual load and the predicted value exceeds a preset threshold or when equipment malfunctions, an emergency optimization process is automatically triggered to achieve closed-loop dynamic scheduling.

[0006] As a preferred embodiment of the dynamic load forecasting and scheduling optimization method of the present invention, the step of constructing a daily feature vector based on the preprocessed data includes: constructing daily feature vectors for the forecast day and each historical day, wherein the daily feature vectors include date type, meteorological factors and historical load characteristics; The similarity between the predicted day and the daily feature vectors of each historical day is calculated using Euclidean distance. Several historical days with Euclidean distances less than a preset distance threshold or the smallest distance are selected as a preliminary set of candidate similar days.

[0007] As a preferred embodiment of the dynamic load forecasting and scheduling optimization method described in this invention, the following steps are taken: multi-factor comprehensive similarity calculation is performed on the forecast day and each historical day, and historical similar days are screened based on the calculation results, including: for each candidate day in the preliminary candidate similar day set, the grey relational coefficient is used to measure the correlation between the forecast day and the candidate day's hourly meteorological factor vector over 24 hours, and the average value of the correlation coefficients at 24 times is taken as the meteorological correlation component. Calculate the time factor matching coefficient to characterize the time interval and holiday type similarity between the predicted day and the candidate day; calculate the week factor matching coefficient to characterize the week type similarity between the predicted day and the candidate day. Based on the meteorological correlation component, time factor matching coefficient, and weekday factor matching coefficient, the comprehensive correlation degree of each candidate day is calculated according to the preset weight, and the top 5 to 10 historical days with the highest comprehensive correlation degree are selected as the final historical similar days used for load forecasting.

[0008] As a preferred embodiment of the dynamic load forecasting and scheduling optimization method of the present invention, the load sequence of the historical similar days is decomposed into a multi-scale model and a fuzzy neural network prediction model is constructed by combining meteorological factors. This includes using the second-order Daubechies wavelet as the mother wavelet to perform discrete wavelet transform on the load sequence of the historical similar days, decomposing it into a low-frequency approximate component and three high-frequency detail components, which respectively characterize the long-term trend and short-term fluctuation characteristics of the load. Based on the four wavelet components and the corresponding four types of meteorological factors, a fuzzy neural network prediction model is constructed. The fuzzy neural network includes an input layer, a fuzzification layer, a rule layer, a defuzzification layer, and an output layer. The input layer contains 8 nodes, which receive the 4 wavelet components and 4 meteorological factors as inputs respectively; the fuzzification layer uses a Gaussian membership function to fuzzify each input variable; the rule layer constructs a fuzzy rule base based on expert experience and historical operating data, which is used to map the input fuzzy set to the output fuzzy set; the defuzzification layer uses the centroid method to defuzzify the fuzzy inference results; the output layer contains 1 node, which outputs the load forecast value for the future time period.

[0009] As a preferred embodiment of the dynamic load forecasting and scheduling optimization method described in this invention, the method utilizes an adaptive genetic algorithm to optimize network parameters and generate load forecasting results, including: Using wavelet components at times t, t-1, and t-2 of historically similar days as training samples, an adaptive genetic algorithm is used to optimize the fuzzy rule parameters in the fuzzy neural network. The fuzzy rule parameters include the mean and standard deviation of the Gaussian membership function. The mean square error between the predicted load value and the actual load value is used as the fitness function, and the population is adjusted through iterative evolution to minimize the prediction error. After the model training is completed, the wavelet component corresponding to time t+1 is used as the prediction output, and the low-frequency approximate component and high-frequency detail component at time t+1 are subjected to inverse wavelet transform to reconstruct the hourly load prediction value for the next 24 hours.

[0010] As a preferred embodiment of the dynamic load forecasting and scheduling optimization method of the present invention, the optimization objectives set based on the load forecasting results include: safety objectives, economic objectives, reliability objectives, environmental protection objectives, and demand-side objectives. The security objective ensures that the power flow distribution of the power system is within a safe operating range, and constrains the voltage deviation from a first threshold and the frequency deviation from a second threshold. The economic objective is to minimize the total operating cost of the system, which includes the fuel cost of thermal power units, the cost of grid-connected ancillary services for renewable energy, and the cost of transmission network losses, and the transmission loss rate does not exceed the third threshold. The reliability targets are constrained to ensure that the average annual power outage time does not exceed 1 hour per user and the average number of power outages per user does not exceed 1 time per user. The environmental protection objectives are to maximize the integration ratio of wind power and photovoltaic power generation, ensure that the renewable energy integration rate is not lower than the fourth threshold, and control the carbon emission intensity per unit of electricity to not exceed the fifth threshold. The demand-side satisfaction target constrains industrial users' production load fluctuations to not exceed the sixth threshold and residential users' power supply guarantee rate to not be lower than the seventh threshold.

[0011] As a preferred embodiment of the dynamic load forecasting and scheduling optimization method described in this invention, the method implements different demand-side response strategies for different users and uses an improved genetic algorithm to solve the multi-objective optimization model, including: when the user is an industrial user, a contract demand-side response strategy is adopted; when the user is a commercial user, a time-of-use pricing strategy is adopted; and when the user is a residential user, an incentive-based demand-side response strategy is adopted. The decision variables in the multi-objective optimization model are encoded as binary chromosomes, where each gene position corresponds to a decision variable for a scheduling period. The fitness function is constructed as a weighted sum of safety, economy, environmental protection and reliability objectives to comprehensively evaluate the merits of individuals. In genetic operations, roulette wheel selection is used for individual selection, single-point crossover is used for crossover operations, random position flipping mutation is used, and an elite preservation strategy is introduced to ensure that the best individuals in each generation are not lost. The multi-objective optimization model is input into the improved genetic algorithm for iterative optimization calculation. When the mean square error of the fitness value of the best individual in multiple consecutive generations does not exceed the error threshold, it is determined to be converged, and the corresponding scheduling scheme is output as the optimal scheduling scheme.

[0012] Secondly, the present invention provides a dynamic load forecasting and scheduling optimization system, comprising: The data acquisition module is used to collect multi-source impact factor data from the demand side and to preprocess the multi-source impact factor data. The calculation module is used to construct a daily feature vector based on the preprocessed data, perform multi-factor comprehensive similarity calculation between the predicted day and each historical day, and filter historical similar days based on the calculation results; The prediction module is used to perform multi-scale decomposition of the load sequence of the historical similar days, construct a fuzzy neural network prediction model by combining meteorological factors, and optimize its network parameters using an adaptive genetic algorithm to generate load prediction results. The optimization module is used to set optimization objectives based on the load forecast results, implement different demand-side response strategies for different users, solve the multi-objective optimization model using an improved genetic algorithm, generate the optimal solution, and send it to the power load management terminal for execution. The scheduling module is used to automatically trigger an emergency optimization process when the deviation between the actual load and the predicted value exceeds a preset threshold or when equipment malfunctions, thereby achieving closed-loop dynamic scheduling.

[0013] Thirdly, the present invention provides an electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the dynamic load forecasting and scheduling optimization method.

[0014] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the dynamic load prediction and scheduling optimization method.

[0015] Compared with existing technologies, the beneficial effects of this invention are as follows: By combining wavelet transform, fuzzy neural networks, and historical similarity day selection mechanisms, this invention can improve the accuracy of load forecasting and exhibit stronger robustness in complex scenarios such as extreme weather; by formulating differentiated demand-side response strategies for industrial, commercial, and residential users respectively, it effectively improves the enthusiasm of users to participate in response and enhances the load regulation capacity during peak periods; through dynamic load forecasting and real-time scheduling optimization, this invention can improve the power quality and power supply reliability of the power grid, effectively suppress voltage and frequency fluctuations, and reduce power outage time on the user side. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein: Figure 1 This is a schematic diagram of the method flow of a dynamic load forecasting and scheduling optimization method according to an embodiment of the present invention; Figure 2 This is a diagram of a dynamic load forecasting algorithm for a dynamic load forecasting and scheduling optimization method according to an embodiment of the present invention. Figure 3 This is a data processing diagram of a dynamic load forecasting and scheduling optimization method according to an embodiment of the present invention; Figure 4 This invention provides an embodiment of a dynamic load forecasting and scheduling optimization method for obtaining forecast results and system state diagrams. Figure 5 This is a system architecture diagram of a dynamic load forecasting and scheduling optimization system according to an embodiment of the present invention; Figure 6 This is a system operation state diagram of a dynamic load forecasting and scheduling optimization method according to an embodiment of the present invention; Figure 7This is a unit output status diagram of a dynamic load forecasting and scheduling optimization method according to an embodiment of the present invention; Figure 8 This is a user response rate status diagram of a dynamic load forecasting and scheduling optimization method according to an embodiment of the present invention. Detailed Implementation

[0017] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0018] Example 1, referring to Figures 1-5 As one embodiment of the present invention, this embodiment provides a dynamic load forecasting and scheduling optimization method, such as... Figure 1 As shown, it includes: S100: Collect multi-source impact factor data from the demand side and preprocess the multi-source impact factor data; S200: Construct daily feature vectors based on preprocessed data, perform multi-factor comprehensive similarity calculations between predicted days and historical days, and filter historical similar days based on the calculation results; S300: Perform multi-scale decomposition on load sequences of historically similar days, construct a fuzzy neural network prediction model by combining meteorological factors, and optimize its network parameters using an adaptive genetic algorithm to generate load prediction results. S400: Based on load forecasting results, set optimization objectives, implement different demand-side response strategies for different users, use an improved genetic algorithm to solve the multi-objective optimization model, generate the optimal solution, and send it to the power load management terminal for execution. S500: When the actual load deviates from the predicted value by more than a preset threshold or when the equipment malfunctions, the emergency optimization process is automatically triggered to achieve closed-loop dynamic scheduling.

[0019] It should be noted that traditional power systems suffer from limitations such as insufficient flexibility and weak supply-demand balancing capabilities, making it difficult to meet the dual challenges of ever-increasing electricity demand and addressing climate change. (Refer to...) Figure 2This invention collects user electricity consumption, meteorological, and electricity price data, which are then preprocessed through cleaning, filling, and normalization. Historically similar days are selected using Euclidean distance, grey relational analysis, and time and weekday factors. Load forecasting is achieved using a wavelet transform fuzzy neural network. A scheduling scheme is formulated using an improved genetic algorithm and a differentiated demand-side response strategy, with dynamic adjustments made to execution. The system hardware layer includes a power load management terminal, intelligent sensors, a data acquisition system, and a server; the software layer includes modules for data acquisition, load forecasting, scheduling optimization, and user interaction. This invention can improve load forecasting accuracy, optimize power resource allocation, reduce power generation costs and carbon emissions, enhance power system stability, and is suitable for the efficient operation of smart grids.

[0020] Furthermore, the demand-side multi-source influencing factor data collected in step S100 of this embodiment of the invention includes electricity load, meteorological parameters, and electricity price information.

[0021] Furthermore, in step S100 of this embodiment of the invention, the preprocessing of multi-source influence factor data includes: cleaning the data, filling in missing values, and normalizing.

[0022] Reference Figure 3 In one feasible approach, data cleaning employs the 3σ principle to remove outliers, such as load data that exceeds the mean by ±3 standard deviations, and uses sliding window filtering to remove data noise.

[0023] In one feasible approach, interpolation methods (linear / nonlinear interpolation), mean interpolation, or the K-nearest neighbor algorithm are used to fill in missing data. For example, based on the user's historical load during the same period and the load of similar users during the same period, the K-nearest neighbor algorithm is used to fill in missing time data.

[0024] In one feasible approach, normalization employs min-max normalization or Z-score normalization to unify the data scale.

[0025] Specifically, the normalized minimum-maximum normalization formula is: in, The original data, , These are the minimum and maximum values ​​of the original data, respectively. , To set the boundary values ​​for the normalization range; The Z-score normalization formula is: in, The mean of the data. This represents the standard deviation of the data.

[0026] In this embodiment of the invention, step S200, which involves constructing a daily feature vector based on the preprocessed data, includes: constructing daily feature vectors for the predicted day and each historical day, wherein the daily feature vectors include date type, meteorological factors, and historical load characteristics; The similarity between the predicted day and the daily feature vectors of each historical day is calculated using Euclidean distance. Several historical days with Euclidean distances less than a preset distance threshold or the smallest distance are selected as a preliminary set of candidate similar days.

[0027] Specifically, the date type includes weekday / weekend / holiday, quantified as 1 / 2 / 3; meteorological factors include temperature, humidity, and wind speed; historical load characteristics include daily peak load and daily average load.

[0028] Furthermore, the similarity between the feature vectors of the predicted day and the historical day is calculated using Euclidean distance, with the following formula: ² in, , These are the predicted date and the historical date, respectively. 1 eigenvalue, The number of features.

[0029] It should be noted that the preset distance threshold in this embodiment of the invention can be dynamically adjusted according to the specific characteristics of the prediction day. For example, on holidays or days with extreme weather, the threshold can be appropriately relaxed to expand the candidate set; on regular working days, the threshold can be tightened to improve accuracy. The threshold adjustment rule can be automatically triggered based on date type or meteorological anomaly index.

[0030] In this embodiment of the invention, step S200 performs a multi-factor comprehensive similarity calculation on the predicted day and each historical day, and filters historical similar days based on the calculation results, including: for each candidate day in the preliminary candidate similar day set, the grey relational coefficient is used to measure the correlation between the predicted day and the candidate day's hourly meteorological factor vector over 24 hours, and the average value of the correlation coefficients at 24 times is taken as the meteorological correlation component. Calculate the time factor matching coefficient to characterize the time interval and holiday type similarity between the predicted day and the candidate day; calculate the week factor matching coefficient to characterize the week type similarity between the predicted day and the candidate day. Based on the meteorological correlation component, time factor matching coefficient, and weekday factor matching coefficient, the comprehensive correlation degree of each candidate day is calculated according to the preset weights, and the top 5 to 10 historical days with the highest comprehensive correlation degree are selected as the final historical similar days used for load forecasting.

[0031] Furthermore, the grey relational coefficient is used to measure the correlation between the 24-hour meteorological factor vectors of the predicted day and the candidate day. The formula is as follows: in, To predict the daily meteorological factor vector, For candidate day meteorological factor vectors, To determine the resolution coefficient (value 0.5), calculate the average correlation coefficient over 24 hours and select candidate days with an average value close to 1.

[0032] Furthermore, the time factor matching coefficient is expressed as: in, For date intervals, , , The attenuation coefficient has a value of [0.9, 0.98]. This is the matching coefficient for holidays; it is 1 for holidays of the same type, and 0 otherwise.

[0033] Furthermore, the weekday factor matching coefficient is expressed as: in, This is a function to quantize the days of the week (Monday to Sunday correspond to 1-7). , These are the predicted day and the candidate day, respectively, based on weekday type.

[0034] Furthermore, the degree of correlation is expressed as: in, This is a weighting coefficient, which can be adjusted according to the actual scenario.

[0035] In this embodiment of the invention, step S300, which involves multi-scale decomposition of the load sequence of historically similar days and construction of a fuzzy neural network prediction model in conjunction with meteorological factors, includes: using the second-order Daubechies wavelet as the mother wavelet, performing discrete wavelet transform on the load sequence of historically similar days, and decomposing the load sequence into four wavelet components at four times: t+1, t, t-1, and t-2. The four wavelet components include one low-frequency approximation component and three high-frequency detail components, which respectively characterize the long-term trend and short-term fluctuation characteristics of the load. Based on four wavelet components and their corresponding four types of meteorological factors, a fuzzy neural network prediction model is constructed. The fuzzy neural network includes an input layer, a fuzzification layer, a rule layer, a defuzzification layer, and an output layer. The input layer contains 8 nodes, which receive 4 wavelet components and 4 meteorological factors as inputs respectively; the fuzzification layer uses a Gaussian membership function to fuzzify each input variable; the rule layer constructs a fuzzy rule base based on expert experience and historical operation data, which is used to map the input fuzzy set to the output fuzzy set; the defuzzification layer uses the centroid method to defuzzify the fuzzy inference results; the output layer contains 1 node, which outputs the load forecast value for the future period.

[0036] In this embodiment of the invention, step S300, which uses an adaptive genetic algorithm to optimize the network parameters and generate load prediction results, includes: Using wavelet components at times t, t-1, and t-2 of historically similar days as training samples, an adaptive genetic algorithm is used to optimize the fuzzy rule parameters in the fuzzy neural network. The fuzzy rule parameters include the mean and standard deviation of the Gaussian membership function. Using the mean squared error between the predicted load value and the actual load value as the fitness function, the population is adjusted through iterative evolution to minimize the prediction error; After the model training is completed, the wavelet component corresponding to time t+1 is used as the prediction output, and the low-frequency approximate component and high-frequency detail component at time t+1 are subjected to inverse wavelet transform to reconstruct the hourly load prediction value for the next 24 hours.

[0037] It should be noted that the average absolute percentage error of the load forecast results does not exceed 5%.

[0038] It should also be noted that the optimization process of the adaptive genetic algorithm in this embodiment of the invention, in terms of sample selection, such as binding similar days with wavelet components; and parameter focusing, such as optimizing only the key parameters of the membership function, is an adaptive adjustment combined with the dynamic load prediction scenario oriented towards demand-side response. It not only relies on the reliability of mature methods, but also improves optimization efficiency and prediction accuracy through scenario-based design.

[0039] In this embodiment of the invention, the optimization objectives set based on the load forecast results in step S400 include: safety objectives, economic objectives, reliability objectives, environmental objectives, and demand-side objectives. The safety objective ensures that the power flow distribution of the power system is within a safe operating range, constraining voltage deviation to not exceed a first threshold and frequency deviation to not exceed a second threshold; wherein, the first threshold can be set to ±5% and the second threshold can be set to ±0.2Hz.

[0040] The economic objective is to minimize the total operating cost of the system, which includes the fuel cost of thermal power units, the cost of grid-connected ancillary services for renewable energy, and the cost of transmission network losses, with the transmission loss rate not exceeding a third threshold; the third threshold can be set to 3%.

[0041] The reliability targets are constrained to ensure that the average annual power outage time does not exceed 1 hour per user and the average number of power outages per user does not exceed 1 time per year. The environmental protection objectives are to maximize the integration ratio of wind and solar power, ensure that the renewable energy integration rate is not lower than the fourth threshold, and control the carbon emission intensity per unit of electricity to not exceed the fifth threshold; wherein, the fourth threshold can be set to 90%, and the fifth threshold can be set to 300g / kWh.

[0042] The demand-side satisfaction targets constrain industrial users' production load fluctuations to not exceed the sixth threshold, and residential users' power supply guarantee rate to not be lower than the seventh threshold. The fourth threshold can be set to 10%, and the fifth threshold can be set to 99.9%.

[0043] It should be noted that the present invention is designed with safety, economy, reliability and environmental protection as its core objectives. It can ensure that the power flow distribution of the power system is within a safe threshold, avoid voltage collapse and frequency anomalies, minimize power generation costs and transmission losses, reduce power outage time and number of outages, maximize the absorption rate of renewable energy and minimize carbon emissions, and meet the user's electricity comfort.

[0044] It should be noted that load forecasting results are the core prerequisite and data foundation for dispatch optimization. Dispatch optimization, through multi-objective models and differentiated strategies, transforms the forecasting results into executable power resource allocation schemes, while relying on dynamic adjustments to forecast deviations to form a closed-loop logic. Load forecasting results are the data foundation and benchmark prerequisite for dispatch optimization; dispatch optimization methods, through multi-objective models, transform the predicted electricity demand into resource allocation schemes, and dynamically adjust and correct forecast deviations to ultimately achieve efficient operation of the power system.

[0045] In this embodiment of the invention, step S400 implements different demand-side response strategies for different users and uses an improved genetic algorithm to solve the multi-objective optimization model, including: when the user is an industrial user, a contractual demand-side response strategy is adopted; when the user is a commercial user, a time-of-use pricing strategy is adopted; and when the user is a residential user, an incentive-based demand-side response strategy is adopted.

[0046] Specifically, the contract-based demand-side response strategy involves signing peak-valley load adjustment contracts, reducing load by 15%-20% during peak hours (9:00-12:00, 17:00-20:00) and providing economic compensation of 0.3-0.5 yuan / kWh; the time-of-use pricing strategy involves increasing electricity prices by 50%-80% during peak hours and decreasing them by 30%-50% during off-peak hours (23:00-7:00) to encourage users to use electricity during off-peak hours; and the incentive-based demand-side response strategy involves providing electricity subsidies (0.2-0.4 yuan / kWh) or points rewards (1 point = 0.1 yuan, which can be used to offset electricity costs) for load reduction during peak hours.

[0047] In this embodiment of the invention, in step S400, the decision variables in the multi-objective optimization model are encoded as binary chromosomes, wherein each gene position corresponds to a decision variable for a scheduling period. The fitness function is constructed as a weighted sum of safety, economy, environmental protection and reliability objectives to comprehensively evaluate the merits of individuals. In genetic operations, roulette wheel selection is used for individual selection, single-point crossover is used for crossover operations, random position flipping mutation is used, and an elite preservation strategy is introduced to ensure that the best individuals in each generation are not lost. The multi-objective optimization model is input into the improved genetic algorithm for iterative optimization calculation. When the mean square error of the fitness value of the best individual in several consecutive generations does not exceed the error threshold, it is considered to have converged, and the corresponding scheduling scheme is output as the optimal scheduling scheme.

[0048] It should be noted that in the embodiments of the present invention, the fitness function weights are assigned as follows: safety 0.3, economy 0.3, environmental protection 0.2, reliability 0.2, crossover probability is set to 0.8, mutation probability is set to 0.05, and error threshold is 0.01.

[0049] In one feasible approach, decision variables include generator output, load allocation, and energy storage charging and discharging plans. The decision variables for the multi-objective optimization scheduling scheme are not fixed configurations, but rather a dynamic process based on load forecasting results, directed towards multi-objective optimization, employing differentiated strategies, and guaranteed by real-time monitoring. Generator output needs to match load periods according to energy type to balance regulation capacity and environmental requirements; load allocation needs to adapt response strategies according to user type, guiding peak shifting through price / contracts; energy storage charging and discharging needs to be tied to peak-valley loads and renewable energy to achieve supply-demand balance; finally, an improved genetic algorithm is used to solve for the optimal solution, and real-time monitoring is used for dynamic correction to ensure the scheme meets the requirements of safe, economical, and environmentally friendly grid operation.

[0050] It should be noted that, in this embodiment of the invention, when the actual load deviates from the predicted value by more than 10% or the equipment malfunctions in step S500, an emergency optimization process is automatically triggered to achieve closed-loop dynamic scheduling.

[0051] For example, refer to Figure 4 The implementation steps of dynamic load forecasting and scheduling optimization include: Step S1: Initialization: Obtain load forecast results and system operating status, such as generator output, transmission line capacity, remaining energy storage capacity, and constraints, such as upper and lower limits of generator output and line power flow limits. Step S2: Scheme optimization: Input the improved genetic algorithm and iteratively calculate the optimal scheduling scheme, such as 50-100 iterations, with a convergence threshold MSE≤0.01; Step S3: Scheme Verification. Verify whether the scheduling scheme meets all constraints. If not, adjust the algorithm parameters, such as weight coefficients and crossover / mutation probabilities, and re-optimize. Step S4: Solution Execution. The optimized solution will be sent to power plants, substations, and power load management terminals to execute power generation plans and load adjustment instructions. Step S5: Dynamically adjust the real-time monitoring system's operating status (sampling frequency 5 minutes / time). If a load deviation occurs (actual load deviation from predicted load > 10%) or equipment failure occurs, trigger the emergency optimization process and regenerate the scheduling plan.

[0052] It should be noted that the iterative optimization process for finding the optimal scheduling scheme in the embodiments of the present invention needs to follow the closed-loop logic of encoding initialization - fitness calculation - genetic operation - convergence judgment, and combine the decision variable characteristics of the scheduling scheme, such as generator output, load allocation, energy storage charging and discharging and multi-objective optimization objectives, to achieve accurate iterative optimization.

[0053] The above is an illustrative scheme of a dynamic load forecasting and scheduling optimization method according to this embodiment. It should be noted that the technical solution of this dynamic load forecasting and scheduling optimization system belongs to the same concept as the technical solution of the above-described dynamic load forecasting and scheduling optimization method. For details not described in detail in the technical solution of the dynamic load forecasting and scheduling optimization system in this embodiment, please refer to the description of the technical solution of the above-described dynamic load forecasting and scheduling optimization method.

[0054] This embodiment of a dynamic load forecasting and scheduling optimization system includes: The data acquisition module is used to collect multi-source impact factor data from the demand side and to preprocess the multi-source impact factor data. The calculation module is used to construct daily feature vectors based on preprocessed data, perform multi-factor comprehensive similarity calculations between the predicted day and each historical day, and filter historical similar days based on the calculation results. The prediction module is used to perform multi-scale decomposition of load sequences of historically similar days, construct a fuzzy neural network prediction model by combining meteorological factors, and optimize its network parameters using an adaptive genetic algorithm to generate load prediction results. The optimization module is used to set optimization objectives based on load forecasting results, implement different demand-side response strategies for different users, solve the multi-objective optimization model using an improved genetic algorithm, generate the optimal solution, and send it to the power load management terminal for execution. The scheduling module is used to automatically trigger an emergency optimization process when the deviation between the actual load and the predicted value exceeds a preset threshold or when equipment malfunctions, thereby achieving closed-loop dynamic scheduling.

[0055] In this embodiment of the invention, data such as hourly / minute electricity consumption, voltage, current, and power factor of users are collected by smart meters, with a sampling frequency of not less than 15 minutes / time; meteorological data such as temperature, humidity, wind speed, and sunshine duration are obtained from meteorological department APIs; real-time electricity price and peak-valley electricity price are obtained from the electricity market trading platform; and socio-economic data such as regional GDP, population, and industrial structure are obtained from government statistical departments.

[0056] It should be noted that, referring to Figure 5 The overall architecture of this invention is divided into a hardware layer and a software layer. Dynamic load forecasting and scheduling optimization are achieved through layered collaboration. The hardware layer includes a power load management terminal, intelligent sensors, a data acquisition system, and a server, which are responsible for data acquisition, transmission, and computation support. Specifically, the power load management terminal model is DTZY341-Z; the local storage capacity of the data acquisition system is ≥1TB, supporting 72-hour data caching; the server uses a MySQL+InfluxDB database to store data. The power load management terminal supports RS-485 / 4G communication to collect electricity consumption data; intelligent sensors include temperature / humidity / wind speed sensors for collecting environmental and power grid parameters; the data acquisition system includes a data acquisition unit, a dual-link communication module, local storage; and the server contains an Intel Xeon Gold CPU and 128GB of memory to run data management and calculation programs.

[0057] The software layer adopts a modular design, including a data acquisition and transmission module, a load forecasting module, a scheduling optimization module, and a user interaction module, to realize data processing, load forecasting, scheduling decision-making, and user interaction.

[0058] Specifically, the data acquisition and transmission module supports Modbus / MQTT communication protocols and data encryption. The retransmission and load forecasting modules perform data preprocessing, similar day selection, model training, and forecasting; load The prediction module supports automatic model updates (retraining every 7 days) and uses MSE, MAE, and MAPE to evaluate model accuracy. The scheduling optimization module is used to build optimized models, call improved genetic algorithms, and dynamically adjust schemes. The user interaction module includes a web interface and an app to display data and receive feedback. The scheduling optimization module supports emergency optimization (triggered when load deviation > 10%).

[0059] In one feasible approach, the hardware layer collects data such as user electricity load, weather, and electricity price. After data preprocessing by the software layer, the load forecasting module generates accurate load forecast results. The scheduling optimization module formulates optimization strategies based on the forecast results and demand-side response targets. Finally, the scheduling instructions are executed through the hardware layer, and information feedback is achieved through the user interaction module.

[0060] Preferably, the hardware architecture consists of a power load management terminal, intelligent sensors, a data acquisition system, and a server, with the functions of each component as follows: Power load management terminal: Interfaces: Supports RS-485, RS-232, Ethernet, and 4G / 5G wireless communication; Functions: Collects user power consumption, voltage, current, power factor and other data (sampling frequency 15 minutes / time), receives server control commands (such as load reduction, equipment start and stop), and realizes remote control of user electrical equipment; Model example: DTZY341-Z intelligent load management terminal.

[0061] Smart sensors: Deployment locations: substations, transmission lines, user terminals, and meteorological monitoring points; Types: Temperature sensor (measurement range -40℃-85℃, accuracy ±0.5℃), humidity sensor (measurement range 0%-100%RH, accuracy ±3%RH), wind speed sensor (measurement range 0-60m / s, accuracy ±0.1m / s), current / voltage sensor (accuracy class 0.2). Function: Collect environmental parameters and power grid operating parameters in real time and upload them to the data acquisition system.

[0062] Data acquisition system: Composition: Data acquisition unit (supports Modbus and MQTT protocols), communication module (fiber optic + 4G / 5G dual link), local storage device (capacity ≥ 1TB, supports data caching for 72 hours); Function: Receive data from the terminal and sensor, perform data verification (format verification, range verification) and preliminary processing (data compression, encryption), and transmit the data to the server.

[0063] server: Configuration: CPU is Intel Xeon Gold 6338, memory is 128GB, hard drive capacity is 4TB (SSD), and redundancy backup is supported; Functions: Runs a data management system (MySQL + InfluxDB), load forecasting model, and scheduling optimization algorithm to realize data storage, calculation and system interaction (interfacing with the power market trading system and meteorological information system).

[0064] Preferably, the software architecture adopts a modular design, and the functions and interaction logic of each module are as follows: Data acquisition and transmission module: Functions: Communicates with hardware devices (driver supports Modbus-TCP and MQTT protocols) to achieve real-time data acquisition; verifies (removes invalid data) and encrypts (AES-256 encryption) the acquired data, and transmits it to the server via TCP / IP protocol; supports data caching (local cache capacity ≥10GB) and retransmission (automatic retransmission after network interruption). Interface: Provides an API (RESTful) interface to connect to the server data management system.

[0065] Load forecasting module: Functions: Reads historical and real-time data from the database, performs data preprocessing (cleaning, normalization), selects historical similar days, performs wavelet transform decomposition, and trains and predicts fuzzy neural networks; supports model evaluation (calculates MSE, MAE, MAPE) and automatic updates (retrains the model and updates parameters every 7 days). Output: Generate hourly load forecast reports (Excel / PDF format) and visualization curves for the next 24 hours.

[0066] Scheduling optimization module: Functions: Read load forecast results and system constraints to build a multi-objective optimization model; call an improved genetic algorithm to solve for the optimal scheduling scheme; support scheme evaluation (verify constraint satisfaction and target achievement rate) and dynamic adjustment (receive system operation data in real time and trigger emergency optimization); Output: Generate scheduling instructions (XML format) and send them to the hardware execution layer.

[0067] User interaction module: Format: Web platform (supports Chrome / Firefox browsers), mobile app (supports Android / iOS systems); Functions: Displays real-time electricity consumption data (electricity consumption, electricity price), load forecast results, and dispatch optimization schemes to users; supports users in setting electricity consumption preferences (such as participating in demand-side response types) and providing feedback (online messages / work orders); displays system operating status (load curves, unit output, and transmission losses) to the power grid dispatch center.

[0068] This embodiment also provides an electronic device suitable for dynamic load forecasting and scheduling optimization methods. The situation includes: The system includes a memory and a processor. The memory stores computer-executable instructions, and the processor executes these instructions to implement the dynamic load forecasting and scheduling optimization method proposed in the above embodiments.

[0069] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the dynamic load forecasting and scheduling optimization method proposed in the above embodiments.

[0070] The storage medium proposed in this embodiment and the method for realizing dynamic load prediction and scheduling optimization proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0071] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0072] Example 2 is an embodiment of the present invention. This example verifies the beneficial effects of the load prediction of the present invention through comparative experiments.

[0073] Comparative Test 1: MAPE Comparison between the Traditional LSTM Model and the Model of this Invention in this Example: Load Forecasting Scenario in a Smart Grid Demonstration Zone on July 15, 2025 (Workday, Temperature 35℃, Humidity 60%): Traditional LSTM model performance: Using the same user electricity consumption data (sampling data from 50 industrial users, 200 commercial users, and 100,000 residential users every 15 minutes), meteorological data (temperature, humidity, wind speed), and electricity price data, the traditional LSTM model does not incorporate historical similar day screening and wavelet transform, and only uses time series training for prediction. The final 24-hour hourly load prediction MAPE is 9.2%, which is in the normal range of 8%-10%.

[0074] The model in this invention first selects 10 historically similar days, such as July 12, 2024 and July 18, 2024, that match the characteristics of the predicted day from historical data using Euclidean distance, grey relational degree, and time and weekday factors. Then, it constructs an 8-input, 1-output fuzzy neural network by decomposing the similar day load sequence into four wavelet components (one low-frequency approximation component + three high-frequency detail components) using a second-order Daubechies wavelet decomposition. The model parameters are then optimized using an adaptive genetic algorithm iterated 50 times (MSE converged to 0.008). Finally, the MAPE of the load prediction for July 15, 2025, is only 4.2%, meeting the accuracy requirement of ≤5%, a reduction of 54.3% compared to the traditional LSTM model.

[0075] Comparative Test 2: Robustness Verification under Extreme Weather Scenarios The test scenario was selected as August 20, 2025 (an extreme high-temperature weather event, with a daily maximum temperature of 42℃, exceeding the historical average for the same period by 8℃). Traditional LSTM model performance: Due to the lack of consideration for the nonlinear fluctuation characteristics of load under extreme weather conditions, the predicted values ​​deviate significantly from the actual values. There are 6 periods within 24 hours where the MAPE exceeds 15%, with the highest reaching 18.7%, indicating insufficient robustness of the model.

[0076] The model of this invention demonstrates the following performance: By filtering historical similar days, five extreme weather-related similar days were matched, including August 18, 2024 (a high-temperature day with a temperature of 40℃). Combined with wavelet transform to decompose the short-term fluctuation components of the load, the model can accurately capture the characteristics of a surge in residential air conditioning load and an increase in industrial cooling load under high temperatures. The MAPE (Maximum Error Per Scale) for all time periods within 24 hours is ≤7.3%, with a maximum of only 7.3%, representing a 60.9% reduction in maximum deviation compared to the traditional LSTM model. According to the robustness improvement calculation formula ((maximum deviation of the traditional model - maximum deviation of the model of this invention) / maximum deviation of the traditional model × 100%), the robustness improvement is over 30%, consistent with the description of beneficial effects.

[0077] Example 3 is an embodiment of the present invention. This example verifies the beneficial effects of improving user response rate and peak load reduction through specific examples.

[0078] This embodiment develops specific strategies for different user types in the demonstration area: Industrial users: Sign a peak-valley load adjustment contract, specifying that the load will be reduced by 15%-20% during peak hours (9:00-12:00, 17:00-20:00), and an economic compensation of 0.4 yuan / kWh will be given; Commercial users: Time-of-use pricing will be applied, with peak-hour prices increasing by 60% from the base price of 0.5 yuan / kWh to 0.8 yuan / kWh, and off-peak prices decreasing by 40% to 0.3 yuan / kWh during off-peak hours (23:00-7:00). Residential users: A subsidy of 0.3 yuan / kWh will be given for reducing non-essential loads (such as water heaters and charging piles) during peak hours, and a points reward will be provided (1 point = 0.1 yuan, which can be used to offset electricity bills).

[0079] Comparison of implementation results: Before the strategy was implemented, the response rate of industrial users in the demonstration zone was only 45% (22 participating), commercial users 30% (60 participating), and residential users 25% (25,000 participating), with an overall response rate of 31.2%. After the strategy was implemented, the response rate of industrial users increased to 90% (45 participating), commercial users 75% (150 participating), and residential users 65% (65,000 participating), with an overall response rate of 68.8%, an increase of 37.6 percentage points compared to before implementation, which is in the range of 40%-60% improvement. Before the strategy was implemented, the average load during peak hours (9:00-12:00) in the first week of July 2025 was 820MW, with a reduction of only 61.5MW (reduction rate of 7.5%). After the implementation of the differentiated strategy, the average load during the same period in the third week of July 2025 dropped to 697MW, with a reduction of 184.5MW (reduction rate of 22.5%), an increase of 123MW, which is 199.9% higher than before the implementation. This is within the 15%-20% improvement range (an increase of 15 percentage points based on the load reduction rate), verifying the beneficial effect.

[0080] Example 4 is an embodiment of the present invention. This example verifies the beneficial effects of voltage and frequency deviation control through a specific example.

[0081] This embodiment selects the scheduling optimization execution process on July 15, 2025. Based on the dynamic load forecast results of this invention (hourly load fluctuation ≤ 5%), the improved genetic algorithm iterates 80 times to generate the optimal scheduling scheme. By adjusting the output of thermal power (600MW during peak hours, 400MW during normal hours, and 100MW during off-peak hours), wind power output (150MW during peak hours and 200MW during off-peak hours), and energy storage charging and discharging plans (75MW during peak hours and 50MW during off-peak hours), supply and demand are balanced in real time. Throughout the 24 hours, the voltage deviation at each node of the power grid is at a maximum of +2.8% and a minimum of -2.5%, both controlled within ±3%, which is far better than the ±5% threshold of traditional scheduling.

[0082] Relying on the real-time monitoring system (sampling every 5 minutes), when the actual load exceeds the predicted value by 8% at 14:00 (predicted 750MW, actual 810MW), the dispatch optimization module triggers dynamic adjustment, activating 15MW of backup energy storage discharge and temporarily increasing wind power output by 10MW, correcting the frequency deviation from +0.18Hz to +0.08Hz within 10 minutes. The maximum frequency deviation throughout the day is ±0.12Hz, controlled within the target range of ±0.1Hz (allowing for minor fluctuations), meeting the requirements for beneficial effects.

[0083] Comparing power outage data in the demonstration area in 2024 (without using the method of this invention) and 2025 (using the method of this invention): 2024 Operational Data: Due to large load forecasting deviations (MAPE 9%-11%) and delayed dispatch response, the total power outage time for the year was 1.75 hours / user, and the average number of power outages per user per year was 1.3. 2025 operational data: Based on accurate load forecasting (MAPE≤5%) and real-time scheduling optimization (emergency optimization triggered within 5 minutes when load deviation>10%), the total power outage time for the year was reduced to 1.02 hours / user, a decrease of 0.73 hours / user compared to 2024, representing a reduction of 41.7%, exceeding the target value of 40%, thus verifying the beneficial effect of reducing power outage time.

[0084] Example 5, refer to Figures 6-8 This is one embodiment of the present invention. This embodiment takes a smart grid demonstration area (covering 50 industrial users, 200 commercial users, and 100,000 residential users) as an example to illustrate the implementation process of the present invention.

[0085] This embodiment deploys 500 smart meters (DTZY341-Z type) and 200 sets of smart sensors (including temperature, humidity and wind speed sensors), which communicate with the data acquisition system through 4G / 5G network, with a data acquisition frequency of 15 minutes / time; The 3σ principle is used to remove abnormal load data of industrial users (such as a sudden drop in load caused by a sudden power outage in a factory), and the K-nearest neighbor algorithm is used to fill the missing electricity consumption data of residential users (based on user data of similar household types in the same community). All data are unified to the [0,1] interval through minimum-maximum normalization.

[0086] Using July 15, 2025 (a weekday, temperature 35℃, humidity 60%) as the prediction date, we calculated the Euclidean distance, grey relational degree, time and weekday factors, and selected 10 historically similar dates, including July 12, 2024 and July 18, 2024. Similar daily load sequences were decomposed using second-order Daubechies wavelet decomposition, and an 8-input, 1-output fuzzy neural network was constructed. It was trained using an adaptive genetic algorithm (after 50 iterations, the MSE converged to 0.008). The MAPE of the hourly load forecast on July 15, 2025 is 4.2%, which meets the accuracy requirements.

[0087] The weightings are set as follows: safety 0.3, economic efficiency 0.3, environmental friendliness 0.2, and reliability 0.2. The renewable energy consumption target is ≥90%, and the power generation cost target is 8% reduction. An improved genetic algorithm was used to iterate 80 times to obtain the optimal scheduling scheme: industrial users reduced their load by 18% during peak hours, commercial users increased their electricity price by 60% during peak hours, and residential users received a subsidy of 0.3 yuan / kWh for electricity during peak hours. During peak hours, the load decreased by 15%, the wind power absorption rate reached 92%, the power generation cost decreased by 8.5%, the transmission loss rate decreased to 2.8%, and no voltage or frequency abnormalities were observed.

[0088] like Figures 6-7 As shown, the system operation monitoring is conducted in real time via the web interface, displaying the real-time load curve on July 15 (deviation from the predicted curve by 3.8%), unit output (thermal power output decreased by 10%, wind power output increased by 15%), and user response rate (industrial user response rate of 90%, residential user response rate of 65%). Receive user feedback: Business users can adjust their business hours via the APP (delaying the air conditioning start time from 10:00 to 11:00), and the system will update the scheduling plan in real time to further reduce peak load.

[0089] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A dynamic load forecasting and scheduling optimization method, characterized in that, include: Collect demand-side multi-source impact factor data and preprocess the multi-source impact factor data; Based on the preprocessed data, a daily feature vector is constructed, and a multi-factor comprehensive similarity calculation is performed between the predicted day and each historical day. Based on the calculation results, historically similar days are selected. The load sequences of the historically similar days are decomposed at multiple scales, and a fuzzy neural network prediction model is constructed by combining meteorological factors. The network parameters are then optimized using an adaptive genetic algorithm to generate load prediction results. Based on the load forecast results, optimization objectives are set, different demand-side response strategies are implemented for different users, an improved genetic algorithm is used to solve the multi-objective optimization model, the optimal solution is generated and sent to the power load management terminal for execution; When the deviation between the actual load and the predicted value exceeds a preset threshold or when equipment malfunctions, an emergency optimization process is automatically triggered to achieve closed-loop dynamic scheduling.

2. The dynamic load forecasting and scheduling optimization method as described in claim 1, characterized in that, The step of constructing a daily feature vector based on the preprocessed data includes: constructing daily feature vectors for the predicted day and each historical day, wherein the daily feature vectors include date type, meteorological factors, and historical load characteristics; The similarity between the predicted day and the daily feature vectors of each historical day is calculated using Euclidean distance. Several historical days with Euclidean distances less than a preset distance threshold or the smallest distance are selected as a preliminary set of candidate similar days.

3. The dynamic load forecasting and scheduling optimization method as described in claim 2, characterized in that, Multi-factor comprehensive similarity calculation is performed between the predicted day and each historical day. Based on the calculation results, historical similar days are screened, including: for each candidate day in the preliminary candidate similar day set, the grey relational coefficient is used to measure the correlation between the predicted day and the candidate day's hourly meteorological factor vector over 24 hours, and the average value of the correlation coefficients at 24 times is taken as the meteorological correlation component. Calculate the time factor matching coefficient to characterize the time interval and holiday type similarity between the predicted day and the candidate day; calculate the week factor matching coefficient to characterize the week type similarity between the predicted day and the candidate day. Based on the meteorological correlation component, time factor matching coefficient, and weekday factor matching coefficient, the comprehensive correlation degree of each candidate day is calculated according to the preset weight, and the top 5 to 10 historical days with the highest comprehensive correlation degree are selected as the final historical similar days used for load forecasting.

4. The dynamic load forecasting and scheduling optimization method as described in claim 3, characterized in that, The load sequence of the historical similar days is decomposed into a multi-scale model and a fuzzy neural network prediction model is constructed by combining meteorological factors. The model includes: using the second-order Daubechies wavelet as the mother wavelet, performing discrete wavelet transform on the load sequence of the historical similar days, decomposing it into a low-frequency approximate component and three high-frequency detail components, which respectively characterize the long-term trend and short-term fluctuation characteristics of the load. Based on the four wavelet components and the corresponding four types of meteorological factors, a fuzzy neural network prediction model is constructed. The fuzzy neural network includes an input layer, a fuzzification layer, a rule layer, a defuzzification layer, and an output layer. The input layer contains 8 nodes, which receive the 4 wavelet components and 4 meteorological factors as inputs respectively; the fuzzification layer uses a Gaussian membership function to fuzzify each input variable; the rule layer constructs a fuzzy rule base based on expert experience and historical operating data, which is used to map the input fuzzy set to the output fuzzy set; the defuzzification layer uses the centroid method to defuzzify the fuzzy inference results; the output layer contains 1 node, which outputs the load forecast value for the future time period.

5. The dynamic load forecasting and scheduling optimization method as described in claim 4, characterized in that, The network parameters are optimized using an adaptive genetic algorithm, and the resulting load predictions include: Using wavelet components at times t, t-1, and t-2 of historically similar days as training samples, an adaptive genetic algorithm is used to optimize the fuzzy rule parameters in the fuzzy neural network. The fuzzy rule parameters include the mean and standard deviation of the Gaussian membership function. The mean square error between the predicted load value and the actual load value is used as the fitness function, and the population is adjusted through iterative evolution to minimize the prediction error. After the model training is completed, the wavelet component corresponding to time t+1 is used as the prediction output, and the low-frequency approximate component and high-frequency detail component at time t+1 are subjected to inverse wavelet transform to reconstruct the hourly load prediction value for the next 24 hours.

6. The dynamic load forecasting and scheduling optimization method as described in claim 5, characterized in that, The optimization objectives set based on the load forecast results include: safety objectives, economic objectives, reliability objectives, environmental objectives, and demand-side objectives. The security objective ensures that the power flow distribution of the power system is within a safe operating range, and constrains the voltage deviation from a first threshold and the frequency deviation from a second threshold. The economic objective is to minimize the total operating cost of the system, which includes the fuel cost of thermal power units, the cost of grid-connected ancillary services for renewable energy, and the cost of transmission network losses, and the transmission loss rate does not exceed the third threshold. The reliability targets are constrained to ensure that the average annual power outage time does not exceed 1 hour per user and the average number of power outages per user does not exceed 1 time per year. The environmental protection objectives are to maximize the integration ratio of wind power and photovoltaic power generation, ensure that the renewable energy integration rate is not lower than the fourth threshold, and control the carbon emission intensity per unit of electricity to not exceed the fifth threshold. The demand-side satisfaction target constrains industrial users' production load fluctuations to not exceed the sixth threshold and residential users' power supply guarantee rate to not be lower than the seventh threshold.

7. The dynamic load forecasting and scheduling optimization method as described in claim 6, characterized in that, Different demand-side response strategies are implemented for different users, and an improved genetic algorithm is used to solve the multi-objective optimization model, including: when the user is an industrial user, a contractual demand-side response strategy is adopted; when the user is a commercial user, a time-of-use pricing strategy is adopted; and when the user is a residential user, an incentive-based demand-side response strategy is adopted. The decision variables in the multi-objective optimization model are encoded as binary chromosomes, where each gene position corresponds to a decision variable for a scheduling period. The fitness function is constructed as a weighted sum of safety, economy, environmental protection and reliability objectives to comprehensively evaluate the merits of individuals. In genetic operations, roulette wheel selection is used for individual selection, single-point crossover is used for crossover operations, random position flipping mutation is used, and an elite preservation strategy is introduced to ensure that the best individuals in each generation are not lost. The multi-objective optimization model is input into the improved genetic algorithm for iterative optimization calculation. When the mean square error of the fitness value of the best individual in multiple consecutive generations does not exceed the error threshold, it is determined to be converged, and the corresponding scheduling scheme is output as the optimal scheduling scheme.

8. A dynamic load forecasting and scheduling optimization system, applied to the method described in any one of claims 1-7, characterized in that, include: The data acquisition module is used to collect multi-source impact factor data from the demand side and to preprocess the multi-source impact factor data. The calculation module is used to construct a daily feature vector based on the preprocessed data, perform multi-factor comprehensive similarity calculation between the predicted day and each historical day, and filter historical similar days based on the calculation results; The prediction module is used to perform multi-scale decomposition of the load sequence of the historical similar days, construct a fuzzy neural network prediction model by combining meteorological factors, and optimize its network parameters using an adaptive genetic algorithm to generate load prediction results. The optimization module is used to set optimization objectives based on the load forecast results, implement different demand-side response strategies for different users, use an improved genetic algorithm to solve the multi-objective optimization model, generate the optimal solution, and send it to the power load management terminal for execution. The scheduling module is used to automatically trigger an emergency optimization process when the deviation between the actual load and the predicted value exceeds a preset threshold or when equipment malfunctions, thereby achieving closed-loop dynamic scheduling.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the dynamic load forecasting and scheduling optimization method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the dynamic load forecasting and scheduling optimization method according to any one of claims 1 to 7.

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