AI-based power grid investment analysis method and system

Abstract

The invention relates to the technical field of power systems, and discloses an AI-based power grid investment analysis method and system, and the system comprises a data collection module, a data processing module, an intelligent prediction module, an optimization investment decision module, a risk assessment module, an intelligent scheduling and visualization module, and a real-time feedback module. Through deep learning, an optimization algorithm and an intelligent evaluation technology, the decision-making precision, flexibility and risk management capability of power grid investment are comprehensively improved, various investment scenes can be supported, the method has strong expansibility and adaptability, dynamic adjustment can be performed according to actual demands and changes of a power grid, and the method is suitable for large-scale popularization and application. And finally, intelligentization, precision and sustainable development of power grid investment are promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to an AI-based power grid investment analysis method and system. Background Art

[0002] As global energy demand continues to grow, the power grid, as the core infrastructure for energy transmission and distribution, faces unprecedented challenges. As the core infrastructure for energy transmission and distribution in modern society, the power grid is responsible for safely and stably transmitting the electricity generated by power plants to various power facilities. With the transformation of the global energy structure and the rapid development of new energy, the upgrade and expansion of the power grid has become more urgent. Power grid investment is not only related to the stability and reliability of the power system, but also involves efficient use of energy, environmental impact and economic benefits. Therefore, power grid investment analysis has become a crucial part of decision-making in the power industry.

[0003] Traditional power grids can no longer meet the growing energy demand, environmental protection requirements and sustainable development goals. Therefore, the construction and upgrading of smart grids has become an important issue in the current power industry.

[0004] In this context, grid investment decision-making has become a complex and critical issue. Grid investment not only involves huge capital investment, but also affects the stability of power supply and sustainable use of energy. Traditional grid investment analysis methods often rely on experience and rule-based models, which have many uncertainties and limitations and cannot effectively cope with the complex and changing market environment. Summary of the invention

[0005] The present invention provides an AI-based power grid investment analysis method and system for solving the technical problems mentioned in the above background technology.

[0006] The present invention provides the following technical solutions:

[0007] AI-based power grid investment analysis system, the system includes:

[0008] Data collection module, used to collect historical investment data, load data, grid operation status, policies and regulations, electricity price information, environmental factors, etc. related to the power grid;

[0009] Data processing module, which is used to clean, normalize, extract features, detect anomalies of the collected data, and build high-quality data sets for analysis;

[0010] Intelligent prediction module, which uses machine learning algorithms to predict grid load, estimate costs, and evaluate investment returns based on historical and real-time data;

[0011] Optimization investment decision-making module, which is used to optimize the power grid investment plan based on artificial intelligence optimization algorithms (such as genetic algorithms, reinforcement learning, etc.) and provide optimal investment suggestions;

[0012] Risk assessment module, which is used to analyze the potential risks of the investment plan and evaluate the impacts of policy changes, market fluctuations, natural disasters, etc. based on methods such as Bayesian networks, fuzzy logic, or neural networks;

[0013] Intelligent scheduling and visualization module, which is used to visually display the power grid investment plan based on the GIS geographic information system and data visualization tools, and support decision-making layer interaction and plan adjustment;

[0014] Real-time feedback module, which is used to dynamically adjust the investment strategy based on the real-time operation data of the power grid to improve investment flexibility.

[0015] 2. The AI-based power grid investment analysis system according to claim 1, wherein: the data acquisition module includes an edge computing unit and a cloud computing unit, which are respectively used for local data preprocessing and cloud data storage and analysis.

[0016] 3. The AI-based power grid investment analysis system according to claim 2, wherein: the intelligent prediction module adopts a deep learning model, including long short-term memory network (LSTM), Transformer model, and autoregressive integrated moving average model (ARIMA).

[0017] 4. The AI-based power grid investment analysis system according to claim 1, wherein: the optimization investment decision-making module adopts a multi-objective optimization method to balance the power grid investment cost, revenue, and power supply reliability. The optimization investment decision-making module is based on reinforcement learning technology, and through training historical data and simulation scenarios, it improves the adaptability and optimality of the investment strategy.

[0018] 5. The AI-based power grid investment analysis system according to claim 1, wherein: the risk assessment module combines big data analysis and expert systems to conduct multi-dimensional intelligent evaluation of the investment plan.

[0019] 6. The AI-based power grid investment analysis system according to claim 1, wherein: the intelligent scheduling and visualization module is based on GIS technology to realize the geographical spatial visualization of the power grid investment plan and provide interactive decision-making support.

[0020] 7. The AI-based power grid investment analysis system according to claim 1, wherein: the real-time feedback module combines Internet of Things technology to dynamically obtain the power grid operation data and adjust the investment plan based on the AI model.

[0021] 8. The AI-based power grid investment analysis system according to claim 1, wherein: the system supports multiple investment scenarios, including transmission grid expansion, distribution grid upgrade, new energy access, energy storage system investment, power grid intelligent transformation, etc.

[0022] 9. The AI-based power grid investment analysis system according to claim 1, wherein: the system has scalability and supports multi-source heterogeneous data access, algorithm upgrade and modular expansion to adapt to different power grid investment requirements.

[0023] 10. The AI-based power grid investment analysis method and system according to claims 1-9, wherein the steps of the power grid investment analysis method are as follows:

[0024] S1. Data collection and preprocessing

[0025] S1.1 Data collection: Collect historical investment data, load data, power grid operation status, policies and regulations, electricity price information, environmental factors, etc. related to the power grid; the data sources can be historical records, real-time data, policy documents, etc., ensuring the comprehensiveness and representativeness of the data;

[0026] S1.2 Data preprocessing: Preprocess the local data, perform data cleaning (removing noise and outliers) and normalization (standardizing data from different sources to a unified scale); transfer the data to the cloud for storage and analysis, and prepare for further data analysis and feature extraction;

[0027] S2. Data processing and feature extraction

[0028] S2.1 Data cleaning: Remove outliers, duplicate values, and missing values in the data, and ensure the integrity and consistency of the data;

[0029] S2.2 Feature extraction: Extract meaningful features from the original data, such as power grid load change trends, investment benefits, etc., and these features will be the input for subsequent analysis;

[0030] S2.3 Anomaly detection: Use statistical or machine learning methods for anomaly detection to identify data points that do not conform to the normal pattern to avoid affecting the accuracy of subsequent analysis;

[0031] S3. Intelligent prediction analysis

[0032] S3.1 Select a prediction model: According to the data type and analysis target collected, select a suitable prediction model, such as LSTM (Long Short-Term Memory Network), Transformer model or ARIMA (Autoregressive Integrated Moving Average Model);

[0033] S3.2 Load Forecasting: Utilize historical load data and employ deep learning models to predict future changes in grid load. This prediction will assist in determining future grid demands and provide support for investment decisions;

[0034] S3.3 Cost Estimation and Investment Return Evaluation: Based on historical and real-time data, use machine learning algorithms to estimate investment costs and evaluate the expected returns of investment projects;

[0035] S3.4 Prediction Result Verification: Verify the accuracy and reliability of the prediction model through backtesting with historical data;

[0036] S4. Investment Decision Optimization

[0037] S4.1 Optimization Objective Setting: Determine optimization objectives, including multiple objectives such as investment cost, investment return, and power supply reliability;

[0038] S4.2 Selection of Optimization Algorithm: Adopt artificial intelligence optimization algorithms, such as genetic algorithms and reinforcement learning, for multi-objective optimization, balance investment costs and returns, and ensure the power supply reliability of the grid;

[0039] S4.3 Optimization Algorithm Training: Train the optimization model through historical data and simulation scenarios to improve its adaptability and prediction accuracy for complex grid investment scenarios

[0040] S4.4 Generation of Optimization Decisions: Generate optimal investment decisions through the optimization algorithm and provide comparisons and recommendations for relevant solutions;

[0041] S5. Risk Assessment

[0042] S5.1 Risk Factor Identification: Based on the investment plan, identify potential risk factors, such as policy changes, market fluctuations, natural disasters, etc.;

[0043] S5.2 Risk Analysis and Modeling: Adopt methods such as Bayesian networks, fuzzy logic, and neural networks to quantitatively analyze potential risks and build a risk assessment model;

[0044] S5.3 Multi-dimensional Risk Assessment: Combine big data analysis and expert systems for multi-dimensional intelligent risk assessment to ensure the robustness and adaptability of investment decisions;

[0045] S6. Intelligent Scheduling and Visualization

[0046] S6.1 GIS Geospatial Modeling: Based on GIS technology, create a geospatial model of the grid investment plan for visual display, showing information such as investment areas and equipment layouts;

[0047] S6.2 Interactive Decision Support: Provide an interactive decision support interface for decision-makers to modify and optimize the power grid investment plan.

[0048] S6.3 Dynamic Visualization Analysis: Real-time display the progress, risk situation, and return analysis of the investment plan, and support dynamic adjustment.

[0049] S7. Real-time Feedback and Dynamic Adjustment

[0050] S7.1 Real-time Data Acquisition: Use Internet of Things technology to acquire real-time power grid operation data, and monitor real-time information such as power grid load and power grid health status.

[0051] S7.2 Dynamic Adjustment of Investment Planning: According to real-time operation data and combined with AI models, dynamically adjust the investment planning to ensure that the investment plan matches the actual power grid operation situation and improve investment flexibility.

[0052] S7.3 Real-time Feedback Optimization: Use the real-time feedback data to adjust the prediction model and optimization algorithm, optimize future investment decisions, and adjust the investment plan.

[0053] S8. Multi-scenario Investment Support

[0054] S8.1 Multi-scenario Analysis: Analyze different power grid investment scenarios, and support the analysis and optimization of multiple investment scenarios such as transmission grid expansion, distribution grid upgrade, new energy access, and energy storage system investment.

[0055] S8.2 Scenario Adaptation and Recommendation: The system automatically recommends the most suitable investment strategy according to different scenario characteristics and conducts refined optimization.

[0056] S9. System Expansion and Modularity

[0057] S9.1 Data Source Access: Support the access of heterogeneous data from different sources to ensure that the system can operate in different power grids. This invention has the following beneficial effects:

[0058] 1. Comprehensive Data Acquisition and Processing Capability

[0059] Combination of Edge Computing and Cloud Computing: Through the collaborative work of the edge computing unit and the cloud computing unit in the data acquisition module, fast preprocessing of local data and storage and analysis of cloud data are realized; this design improves the data processing efficiency of the system, reduces latency, and can perform large-scale data analysis in the cloud to provide more efficient decision support.

[0060] Construction of High-quality Data Sets: The data processing module ensures the high quality of data through methods such as data cleaning, normalization, feature extraction, and anomaly detection, avoiding the impact of inaccurate or incomplete data on the analysis results, and thus providing a reliable basis for subsequent prediction and decision-making.

[0061] 2. Precise intelligent prediction ability

[0062] Multiple deep learning models: The intelligent prediction module adopts deep learning models such as Long Short-Term Memory Network (LSTM), Transformer model, and Autoregressive Integrated Moving Average model (ARIMA), which enables the system to accurately predict key indicators such as power grid load, cost, and investment return; by processing historical data and real-time data, the system can accurately evaluate the future demand of the power grid and provide a scientific basis for investment decisions.

[0063] Enhanced prediction accuracy and reliability: Deep learning models can process a large amount of complex data and capture the non-linear characteristics in the operation of the power grid, improving the accuracy and reliability of prediction, and thus making the power grid investment plan more accurate.

[0064] 3. Optimized investment decision-making and risk assessment

[0065] Multi-objective optimization method: The optimized investment decision-making module adopts a multi-objective optimization algorithm, which can comprehensively consider multi-dimensional factors such as investment cost, return, and power supply reliability, ensuring that investment decisions can be balanced among multiple objectives and avoiding the risks that may be brought by single-objective optimization. Combining reinforcement learning technology, the system can continuously optimize investment strategies by training historical data and simulated scenarios, improving the adaptability and optimality of decision-making.

[0066] Multi-dimensional risk assessment: The risk assessment module can evaluate the potential risks of investment plans from multiple angles by combining big data analysis and expert systems, including external factors such as policy changes, market fluctuations, and natural disasters. Through intelligent assessment, the system can fully identify risks before investment decisions and reduce the uncertainty in the investment process.

[0067] 4. Visualization and real-time feedback

[0068] Geospatial visualization of GIS technology: The intelligent scheduling and visualization module is based on GIS technology to visually display the geographical information of the power grid investment plan, enabling decision-makers to intuitively understand the geographical layout of the power grid investment plan, which not only improves the visibility of investment decisions but also enhances the interactivity and flexibility of decision-making.

[0069] Dynamic adjustment and real-time feedback: The real-time feedback module combines Internet of Things technology to obtain the operation data of the power grid in real time and dynamically adjusts investment strategies according to the analysis of the AI model. This real-time adjustment ability can ensure that the investment plan is always adapted to the actual operation state, improving the flexibility and responsiveness of decision-making.

[0070] 5. Adaptability to multiple investment scenarios and flexibility

[0071] Multi-scenario Investment Support: This system supports multiple power grid investment scenarios, such as transmission grid expansion, distribution grid upgrade, new energy access, energy storage system investment, etc. The multi-scenario adaptability of the system enables it to flexibly respond to different types of power grid investment needs, enhancing the versatility and application scope of the system.

[0072] Scalability of the System: The system is designed with strong scalability, supporting the access of multi-source heterogeneous data, algorithm upgrade, and module expansion. As the power grid investment needs change, the system can quickly adapt to new technologies and requirements, ensuring its long-term effectiveness and expansion potential.

[0073] 6. Improve Decision-making Efficiency and Accuracy

[0074] Optimize the Decision-making Process: Combining AI algorithms and intelligent optimization technologies, the system can quickly provide the optimal solution in complex power grid investment decisions, greatly reducing the decision-making time and improving the decision-making efficiency. At the same time, through multi-dimensional analysis and optimization, the system improves the accuracy of investment decisions, ensuring the efficiency and reliability of decisions.

[0075] Reduce Risks and Costs: Through intelligent risk assessment and real-time feedback, the system can effectively identify and avoid potential investment risks, reduce costs and uncertainties in the investment process, and ensure the robustness of power grid investment.

[0076] 7. Promote Power Grid Intelligence and Sustainable Development

[0077] Drive the Intelligent Transformation of the Power Grid: The system design not only supports traditional power grid investment projects but also supports innovative investment projects such as the intelligent transformation of the power grid and new energy access. Through the application of intelligent technologies, the power grid can meet current needs while also coping with the challenges of future energy transformation.

[0078] Improve the Ability of Sustainable Development: The system can ensure the maximization of power grid investment benefits, improve energy utilization efficiency, and promote the sustainable development of the power grid through precise investment planning and real-time dynamic adjustment.

[0079] The AI-based power grid investment analysis system comprehensively improves the decision-making accuracy, flexibility, and risk management ability of power grid investment through deep learning, optimization algorithms, and intelligent evaluation technologies. It can not only support multiple investment scenarios but also has strong scalability and adaptability, and can be dynamically adjusted according to the actual needs and changes of the power grid, ultimately promoting the intelligent, precise, and sustainable development of power grid investment. Brief Description of the Drawings

[0080] Figure 1 It is a schematic diagram of the system architecture of the present invention;

[0081] Figure 2Schematic diagram of the load forecasting model of the present invention;

[0082] Figure 3 Schematic diagram of the optimization decision-making process of the present invention. Detailed implementation manners

[0083] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0084] Embodiment

[0085] Please refer to Figures 1 to 3 , an AI-based power grid investment analysis system, the system includes:

[0086] A data acquisition module for collecting historical investment data related to the power grid (including past investment records of the power grid, such as equipment updates, expansion projects, and technical transformations, to help analyze past investment patterns), load data (collecting real-time load information of the power grid to understand the usage of the power grid at different time periods and predict future load demands), power grid operation status (monitoring the health status and operation efficiency of the power grid, such as power transmission, transformer load, power loss, etc., for operation evaluation), policies and regulations, electricity price information (collecting information such as policy changes, regulation updates, and electricity price adjustments, which helps to evaluate the external environment and economic benefits of investment), environmental factors (including environmental change factors such as climate and natural disasters, which have a significant impact on the operation of the power grid and need to be considered in decision-making), etc.; the core task of this module is to collect power grid-related data from various sources to ensure the comprehensiveness and accuracy of system analysis.

[0087] A data processing module for cleaning the collected data (deleting duplicate, inconsistent, and missing data to ensure the integrity of the analysis data), normalizing (since the data dimensions and ranges from different sources are different, the normalization process converts various types of data into a unified scale to ensure balance when used in the model), feature extraction (extracting meaningful features from the original data, such as load fluctuations, return on investment, etc.), anomaly detection (using statistical methods or machine learning algorithms to detect outliers in the data to adjust in a timely manner and improve the accuracy of subsequent analysis), and constructing a high-quality data set for analysis; the data processing module constructs a high-quality data set for subsequent analysis.

[0088] The intelligent prediction module is used to perform power grid load forecasting based on historical data and real-time data using machine learning algorithms (forecasting the time series of power grid load using deep learning models such as LSTM and Transformer based on historical data and real-time data, providing a decision-making basis for future power grid construction), cost estimation (estimating the costs of power grid construction and operation using regression analysis or deep learning models based on historical investment data and market information), and investment return evaluation (evaluating the economic benefits of a project by establishing a return prediction model and combining the capital investment, operation costs, and long-term returns of power grid construction); the intelligent prediction module uses machine learning algorithms to predict the future operating conditions of the power grid.

[0089] The optimized investment decision-making module is used to optimize the power grid investment plan based on artificial intelligence optimization algorithms (such as genetic algorithms, reinforcement learning, etc.) and provide optimal investment suggestions; when making investment decisions, not only investment costs and expected returns need to be considered, but also multiple factors such as the power supply reliability and operation efficiency of the power grid need to be considered; through repeated training with historical data and simulation scenarios, the optimization algorithm can be adapted to different investment environments and power grid requirements, ensuring that the investment decision has self-adaptive capabilities.

[0090] The risk assessment module is used to analyze the potential risks of the investment plan and evaluate policy changes (evaluating the impact of policy and regulatory changes on the investment plan to ensure that the investment strategy can adapt to policy changes), market fluctuations (analyzing the impact of electricity price fluctuations, energy market changes, etc. on power grid investment), natural disasters, etc. (using climate change data, natural disaster predictions, etc. to evaluate the risks of power grid facilities in the face of natural disasters such as extreme weather); quantitatively analyze multiple risks and conduct comprehensive evaluations by combining big data and expert systems to ensure that investment decisions can address potential risks.

[0091] The intelligent scheduling and visualization module is used to visually display the power grid investment plan based on the GIS geographic information system (through GIS technology, visualizing information such as the geographical distribution, construction area, and equipment layout of the power grid investment plan to help decision-makers better understand the actual situation of the investment area), data visualization tools, and support decision-making layer interaction (providing an interactive decision support system based on a visual interface, enabling decision-makers to intuitively view the effects of the power grid investment plan and adjust the investment plan in real time) and plan adjustment; providing an interactive interface and adjustable analysis views for decision-makers.

[0092] A real-time feedback module, which is used to dynamically adjust the investment strategy based on the real-time operation data of the power grid (dynamically collect the real-time operation status of the power grid through sensors and IoT devices, including data such as current, voltage, and load), and improve the investment flexibility by adjusting the power grid investment strategy based on real-time data feedback to ensure that the investment plan can flexibly respond to the changes in the power grid operation and avoid over-investment or under-investment).

[0093] Specifically, the data acquisition module includes an edge computing unit and a cloud computing unit, which are respectively used for local data preprocessing and cloud data storage and analysis; the main function of the edge computing unit is to perform preliminary preprocessing and analysis on data near the data acquisition source, located at the "edge" of data generation. Compared with traditional centralized cloud computing, it can respond to local data processing requirements faster, reduce data transmission latency, and relieve the computing pressure on the cloud; the cloud computing unit is responsible for the storage, in-depth analysis and calculation of large-scale data, can handle complex machine learning algorithms, optimization models and risk assessments, and at the same time ensure the long-term storage and sharing of data.

[0094] Specifically, the intelligent prediction module uses deep learning models, including Long Short-Term Memory (LSTM) networks (a type of Recurrent Neural Network (RNN) specially designed to process and predict sequential data, which can effectively capture long-term dependencies in time series and avoid the problem of gradient vanishing or explosion in traditional RNNs), Transformer models (a type of deep learning model based on self-attention mechanism, initially used for natural language processing tasks, but due to its strong parallel computing ability and the advantage of capturing long-distance dependencies, it has been widely applied in fields such as time series prediction), and Autoregressive Integrated Moving Average (ARIMA) models (a classic statistical model widely used for the analysis and prediction of time series data, which models based on the historical values and errors of the time series data itself and is suitable for processing stationary time series data).

[0095] The advantage of the Long Short-Term Memory (LSTM) network is that LSTM can remember information over a long time span and is suitable for processing data with long-term dependencies such as power grid load. Compared with traditional RNNs, LSTM can effectively avoid the problem of gradient vanishing or explosion and ensure the stability of long-term prediction; the advantage of the Transformer is its strong parallel computing ability, which is suitable for processing large-scale data sets, improving the computing efficiency. The self-attention mechanism can effectively capture complex dependencies in time series and adapt to complex prediction tasks such as power grid load changes; the advantage of the Autoregressive Integrated Moving Average (ARIMA) model is that the ARIMA model structure is simple and suitable for modeling uncomplicated or stationary time series. Compared with deep learning models, ARIMA is more suitable for scenarios with less data and short-term demand prediction.

[0096] Specifically, the optimized investment decision-making module adopts a multi-objective optimization method to balance the grid investment cost, revenue, and power supply reliability. Based on reinforcement learning technology, the optimized investment decision-making module improves the adaptability and optimality of investment strategies through training historical data and simulated scenarios.

[0097] In the grid investment decision-making, cost and revenue are usually the most direct objectives, while power supply reliability is the key to long-term development. Through a multi-objective optimization model, these objectives can be weighed to find an optimal solution that balances all objectives. Common multi-objective optimization methods include the weighted sum method. By setting weights for different objectives, multiple objectives are converted into a single objective for optimization, thus simplifying the optimization process. This requires allocating appropriate weight values according to the priorities of different projects.

[0098] Reinforcement learning technology is trained through historical data, including the grid's load data, investment data, and operation data, etc. By deriving the optimal strategy based on historical data, it can not only handle known situations but also adapt to possible future changes. The reinforcement learning model improves the flexibility and optimality of decision-making through repeated interactions with simulated scenarios, ensuring that it can quickly make adjustments and optimize decisions when facing complex grid investment decisions.

[0099] Specifically, the risk assessment module combines big data analysis (big data analysis provides data support for risk assessment by processing a large amount of historical data and real-time data) with an expert system (the expert system provides a more professional and detailed analysis for risk assessment by simulating the decision-making thinking of experts and combining a large amount of rules and empirical knowledge) to conduct multi-dimensional intelligent assessment of investment plans (policy change risk, market fluctuation risk, natural disaster risk, technical failure risk, and environmental factor risk).

[0100] The risk assessment by big data analysis is mainly carried out through the following aspects:

[0101] Multi-source data fusion: Grid investment involves data from multiple fields, such as policies and regulations, grid operation status, market fluctuations, electricity price information, environmental factors, etc. Big data analysis can collect and integrate these data from different sources to create a comprehensive dataset, helping to identify and analyze potential risk factors; Data mining: By applying data mining techniques (such as clustering analysis, association rule mining, classification analysis, etc.), potential patterns and regularities can be discovered from historical data. These patterns help to identify various risks that may be faced in grid investment, such as market price fluctuations, changes in energy demand, or equipment failures, etc.; Real-time data monitoring: By monitoring the operation status of the grid and changes in the external environment in real time, big data analysis can immediately identify factors that may trigger risks. For example, by monitoring grid load data, equipment operation data, etc. in real time, risks such as grid failures and abnormal loads can be detected early and early warnings can be issued; Predictive analysis: Big data analysis not only focuses on existing data but also includes predictions of future trends. Using big data tools, future electricity demand, market price fluctuations, policy changes, etc. can be predicted, providing forward-looking support for risk management.

[0102] Specifically, the intelligent scheduling and visualization module is based on GIS technology to achieve the geospatial visualization of the grid investment plan (GIS technology can combine the data in the grid investment plan (such as grid facilities, load points, substation locations, line layouts, etc.) with geospatial information to create three-dimensional or two-dimensional maps, providing the spatial distribution of grid investment), and provide interactive decision-making support (through an interactive interface, decision-makers can perform various interactive operations with the system, such as viewing detailed information of grid facilities, adjusting investment strategies, changing resource allocation, etc. Based on operations such as clicking, zooming, and dragging on the map, users can flexibly query, select, and make decisions on spatial data).

[0103] Specifically, the real-time feedback module combines Internet of Things technology to dynamically obtain grid operation data and adjusts the investment plan based on the AI model; the real-time feedback module is a key part of the grid investment analysis system. By combining Internet of Things (IoT) technology and AI models, it can dynamically obtain grid operation data and adjust the investment plan in real time according to these data to improve investment flexibility and adaptability. The core goal of the feedback module is to ensure that grid investment is closely linked to the actual operation status and can cope with the impacts of factors such as grid load fluctuations, equipment failures, and policy changes, thereby optimizing investment strategies and ensuring the effective use of funds and the long-term stable operation of the grid.

[0104] Specifically, the system supports multiple investment scenarios, including transmission grid expansion (which usually involves adding or optimizing existing transmission lines and substations to cope with the growth of electricity demand or improve the stability of the power grid), distribution grid upgrade (the aim of distribution grid upgrade is to improve the power supply reliability, automation level and service quality of the grid. Such investments involve replacing aging equipment, increasing distribution automation, improving power quality, etc.), new energy access (as the proportion of new energy such as solar energy and wind energy in the grid continues to increase, the grid needs to be correspondingly transformed to adapt to the volatility and intermittency of these new energy sources), energy storage system investment (energy storage system investment is to improve the adaptability of the grid to fluctuating loads and renewable energy sources and ensure the stability of power supply), grid intelligent transformation (grid intelligent transformation increases intelligent sensors, automation equipment, communication networks, etc. to improve the automation and intelligent level of the grid, enabling the grid to monitor itself, regulate itself, detect faults automatically, etc.).

[0105] Specifically, the system has scalability and supports multi-source heterogeneous data access, algorithm upgrade and modular expansion to adapt to different grid investment needs; the system's scalability enables the grid investment analysis system to be flexibly adjusted and expanded according to actual needs to meet grid investment analysis tasks of different scales and complexities. The scalability of the system not only includes data access, algorithm upgrade and module expansion, but also the ability to flexibly adapt to different grid investment needs, ensuring that the system can operate efficiently and provide accurate analysis in various investment scenarios. Specifically, the scalability of the system is manifested in the following aspects: multi-source heterogeneous data access, algorithm upgrade and optimization, modular expansion, adaptation to different grid investment needs, support for emerging technologies and future development, and multi-scenario support.

[0106] The AI-based method and system for grid investment analysis, the steps of the grid investment analysis method are as follows:

[0107] S1. Data collection and preprocessing

[0108] S1.1 Data collection: Collect historical investment data, load data, grid operation status, policies and regulations, electricity price information, environmental factors, etc. related to the power grid; the data sources can be historical records, real-time data, policy documents, etc., ensuring the comprehensiveness and representativeness of the data;

[0109] S1.2 Data preprocessing: Preprocess the local data, perform data cleaning (removing noise and outliers) and normalization (standardizing data from different sources to a unified scale); transmit the data to the cloud for storage and analysis, and prepare for further data analysis and feature extraction;

[0110] S2. Data processing and feature extraction

[0111] S2.1 Data cleaning: Remove outliers, duplicate values, and missing values from the data, and ensure the integrity and consistency of the data;

[0112] S2.2 Feature extraction: Extract meaningful features from the original data, such as the changing trend of power grid load, investment benefits, etc. These features will serve as the input for subsequent analysis;

[0113] S2.3 Anomaly detection: Use statistical or machine learning methods for anomaly detection to identify data points that do not conform to the normal pattern, so as to avoid affecting the accuracy of subsequent analysis;

[0114] S3. Intelligent prediction analysis

[0115] S3.1 Select a prediction model: According to the type of collected data and the analysis goal, select a suitable prediction model, such as LSTM (Long Short-Term Memory Network), Transformer model, or ARIMA (Autoregressive Integrated Moving Average Model);

[0116] S3.2 Load prediction: Use historical load data and a deep learning model to predict future changes in power grid load. This prediction will help judge future power grid demand and provide support for investment decisions;

[0117] S3.3 Cost estimation and investment return evaluation: Based on historical data and real-time data, use machine learning algorithms to estimate the investment cost and evaluate the expected return of investment projects;

[0118] S3.4 Verification of prediction results: Verify the accuracy and reliability of the prediction model through backtesting of historical data;

[0119] S4. Investment decision optimization

[0120] S4.1 Setting optimization goals: Determine optimization goals, including multiple goals such as investment cost, investment return, and power supply reliability;

[0121] S4.2 Select an optimization algorithm: Use artificial intelligence optimization algorithms, such as genetic algorithms, reinforcement learning, etc., for multi-objective optimization, balance investment cost and return, and ensure the power supply reliability of the power grid;

[0122] S4.3 Training of optimization algorithm: Train the optimization model through historical data and simulation scenarios to improve its adaptability and prediction accuracy for complex power grid investment scenarios

[0123] S4.4 Generate optimized decisions: Generate optimal investment decisions through the optimization algorithm, and provide comparisons and recommendations for relevant solutions;

[0124] S5. Risk assessment

[0125] S5.1 Risk factor identification: Identify potential risk factors according to the investment plan, such as policy changes, market fluctuations, natural disasters, etc.;

[0126] S5.2 Risk analysis and modeling: Adopt methods such as Bayesian networks, fuzzy logic, neural networks, etc. to quantitatively analyze potential risks and build a risk assessment model;

[0127] S5.3 Multi-dimensional risk assessment: Combine big data analysis and expert systems to conduct multi-dimensional intelligent risk assessment to ensure the robustness and adaptability of investment decisions;

[0128] S6. Intelligent scheduling and visualization

[0129] S6.1 GIS geospatial modeling: Based on GIS technology, create a geospatial model of the power grid investment plan for visual display, showing information such as investment areas and equipment layouts;

[0130] S6.2 Interactive decision support: Provide an interactive decision support interface for decision-makers to modify and optimize the power grid investment plan

[0131] S6.3 Dynamic visualization analysis: Real-time display the progress, risk situation and income analysis of the investment plan, and support dynamic adjustment;

[0132] S7. Real-time feedback and dynamic adjustment

[0133] S7.1 Real-time data acquisition: Use Internet of Things technology to obtain real-time power grid operation data, and monitor real-time information such as power grid load and power grid health status;

[0134] S7.2 Dynamically adjust the investment plan: According to the real-time operation data, combine with the AI model to dynamically adjust the investment plan to ensure that the investment plan matches the actual power grid operation situation and improve investment flexibility;

[0135] S7.3 Real-time feedback optimization: Use the real-time feedback data to adjust the prediction model and optimization algorithm, optimize future investment decisions and adjust the investment plan;

[0136] S8. Multi-scenario investment support

[0137] S8.1 Multi-scenario analysis: Analyze different power grid investment scenarios, and support the analysis and optimization of multiple investment scenarios such as transmission grid expansion, distribution grid upgrade, new energy access, and energy storage system investment;

[0138] S8.2 Scenario adaptation and recommendation: The system automatically recommends the most suitable investment strategy according to different scenario characteristics and conducts refined optimization;

[0139] S9. System expansion and modularization

[0140] S9.1 Data source access: Support the access of heterogeneous data from different sources to ensure the system in different power grids.

[0141] The advantages of the power grid investment analysis method lie in

[0142] Optimized decision-making: Through steps such as data collection, intelligent prediction, risk assessment, and optimized decision-making, the entire system can optimize power grid investment decisions in multiple objectives and dimensions. The decision not only considers cost-effectiveness but also comprehensively considers power supply reliability and risk factors to ensure the comprehensiveness of the decision.

[0143] Risk reduction: Based on real-time feedback and dynamic adjustment, the system can identify and reduce the uncertainties in investment decisions, enhancing the safety of power grid investment.

[0144] Efficiency improvement: Processes such as automated data processing, predictive analysis, and investment optimization significantly improve decision-making efficiency, reducing the complexity and time cost of manual operations.

[0145] Enhanced flexibility and adaptability: The intelligent features of the system enable it to make rapid adjustments based on real-time data and the changing market environment, enhancing the flexibility and adaptability of power grid investment planning and avoiding investment mistakes caused by market changes.

[0146] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device.

[0147] The above are only the preferred embodiments of the present invention. It should be pointed out that for those of ordinary skill in the art of this technology, without departing from the technical principles of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. The AI-based power grid investment analysis system is characterized by: include: Data collection module, used to collect historical investment data, load data, grid operation status, policies and regulations, electricity price information, environmental factors, etc. related to the power grid; Data processing module, which is used to clean, normalize, extract features, detect anomalies of the collected data, and build high-quality data sets for analysis; Intelligent prediction module, which uses machine learning algorithms to predict grid load, estimate costs, and evaluate investment returns based on historical and real-time data; Optimization investment decision module, which is used to optimize the power grid investment plan based on artificial intelligence optimization algorithms (such as genetic algorithms, reinforcement learning, etc.) and provide the best investment recommendations; Risk assessment module, which is used to analyze the potential risks of investment solutions and evaluate the impact of policy changes, market fluctuations, natural disasters, etc. based on methods such as Bayesian networks, fuzzy logic or neural networks; Intelligent dispatching and visualization module, which is used to visualize the power grid investment plan based on GIS geographic information system and data visualization tools, and supports decision-making layer interaction and plan adjustment; The real-time feedback module is used to dynamically adjust investment strategies based on real-time grid operation data to improve investment flexibility.

2. The AI-based power grid investment analysis system according to claim 1, characterized in that: The data acquisition module includes an edge computing unit and a cloud computing unit, which are used for local data preprocessing and cloud data storage and analysis respectively.

3. The AI-based power grid investment analysis system according to claim 2 is characterized in that: The intelligent prediction module adopts a deep learning model, including a long short-term memory network (LSTM), a Transformer model, and an autoregressive integrated moving average model (ARIMA).

4. The AI-based power grid investment analysis system according to claim 1, characterized in that: The optimized investment decision module adopts a multi-objective optimization method to balance the investment cost, income and power supply reliability of the power grid. The optimized investment decision module is based on reinforcement learning technology and improves the adaptability and optimality of the investment strategy by training historical data and simulating scenarios.

5. The AI-based power grid investment analysis system according to claim 1, characterized in that: The risk assessment module combines big data analysis with expert systems to conduct multi-dimensional intelligent assessment of investment plans.

6. The AI-based power grid investment analysis system according to claim 1, characterized in that: The intelligent dispatching and visualization module is based on GIS technology to realize the geographic spatial visualization of power grid investment plans and provide interactive decision support.

7. The AI-based power grid investment analysis system according to claim 1, characterized in that: The real-time feedback module combines Internet of Things technology to dynamically obtain power grid operation data and adjust investment plans based on AI models.

8. The AI-based power grid investment analysis system according to claim 1, characterized in that: The system supports a variety of investment scenarios, including transmission network expansion, distribution network upgrade, new energy access, energy storage system investment, and intelligent transformation of the power grid.

9. The AI-based power grid investment analysis system according to claim 1, characterized in that: The system is scalable and supports multi-source heterogeneous data access, algorithm upgrades and modular expansion to meet different power grid investment needs.

10. The AI-based power grid investment analysis method and system according to claims 1-9, characterized in that: The steps of power grid investment analysis method are as follows: S1. Data collection and preprocessing S1.1 Data collection: Collect historical investment data, load data, grid operation status, policies and regulations, electricity price information and environmental factors related to the power grid; data sources can be historical records, real-time data, policy documents, etc., to ensure the comprehensiveness and representativeness of the data; S1.2 Data preprocessing: Preprocess local data, perform data cleaning (remove noise and outliers) and normalization (standardize data from different sources to a unified scale); transfer data to the cloud for storage and analysis, and prepare for further data analysis and feature extraction; S2. Data processing and feature extraction S2.1 Data cleaning: remove outliers, duplicate values, and missing values ​​from the data, and ensure the integrity and consistency of the data; S2.2 Feature extraction: Extract meaningful features from the raw data, such as grid load change trends, investment benefits, etc. These features will become the input for subsequent analysis; S2.3 Anomaly detection: Use statistical or machine learning methods to detect anomalies and identify data points that do not conform to normal patterns to avoid affecting the accuracy of subsequent analysis; S3. Intelligent Prediction Analysis S3.1 Select the prediction model: According to the collected data type and analysis objectives, select the appropriate prediction model, such as LSTM (Long Short-Term Memory Network), Transformer model or ARIMA (Autoregressive Integrated Moving Average Model); S3.2 Load forecasting: Using historical load data and deep learning models to predict future grid load changes, this forecast will help determine future grid demand and provide support for investment decisions; S3.3 Cost estimation and investment return evaluation: Based on historical data and real-time data, use machine learning algorithms to estimate investment costs and evaluate the expected returns of investment projects; S3.4 Forecast result verification: verify the accuracy and reliability of the forecast model through backtesting of historical data; S4. Investment decision optimization S4.1 Optimization target setting: Determine the optimization target, including multiple targets such as investment cost, investment return and power supply reliability; S4.2 Select optimization algorithm: Use artificial intelligence optimization algorithms, such as genetic algorithms and reinforcement learning, to perform multi-objective optimization, balance investment costs and benefits, and ensure the power supply reliability of the power grid; S4.3 Optimization algorithm training: Optimize the model through historical data and simulation scenario training to improve its adaptability and prediction accuracy for complex power grid investment scenarios S4.4 Generate optimized decisions: Generate optimal investment decisions through optimization algorithms, and provide comparison and recommendation of relevant plans; S5. Risk Assessment S5.1 Risk factor identification: Based on the investment plan, identify potential risk factors, such as policy changes, market fluctuations, natural disasters, etc.; S5.2 Risk analysis and modeling: Use Bayesian networks, fuzzy logic, neural networks and other methods to quantitatively analyze potential risks and build risk assessment models; S5.3 Multi-dimensional risk assessment: Combine big data analysis with expert systems to conduct multi-dimensional intelligent risk assessment to ensure that investment decisions are robust and adaptable; S6. Intelligent scheduling and visualization S6.1GIS geospatial modeling: Based on GIS technology, create a geospatial model of the power grid investment plan and visualize it to display information such as various investment areas and equipment layout; S6.2 Interactive Decision Support: Provides an interactive decision support interface for decision makers to modify and optimize the power grid investment plan S6.3 Dynamic Visual Analysis: Real-time display of investment plan progress, risk situation and return analysis, supporting dynamic adjustment; S7. Real-time feedback and dynamic adjustment S7.1 Real-time data acquisition: Use IoT technology to obtain real-time grid operation data and monitor real-time information such as grid load and grid health status; S7.2 Dynamically adjust investment plans: Dynamically adjust investment plans based on real-time operation data and AI models to ensure that investment plans match actual grid operation conditions and improve investment flexibility; S7.3 Real-time feedback optimization: Use real-time feedback data to adjust forecasting models and optimization algorithms, optimize future investment decisions and adjust investment plans; S8. Multi-scenario investment support S8.1 Multi-scenario analysis: Analyze different power grid investment scenarios, support analysis and optimization of multiple investment scenarios such as transmission network expansion, distribution network upgrade, new energy access, and energy storage system investment; S8.2 Scenario Adaptation and Recommendation: The system automatically recommends the most appropriate investment strategy based on the characteristics of different scenarios and performs refined optimization; S9. System expansion and modularity S9.1 Data source access: Support heterogeneous data access from different sources to ensure that the system is in different power grids.

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