Energy risk analysis method and system based on time dependence model

Abstract

The invention relates to the technical field of energy risk analysis, in particular to an energy risk analysis method and system based on a time dependence model, and the method comprises the steps: defining risk measurement, and obtaining related data affecting the risk measurement; establishing a time dependence model according to the obtained related data; and risk prediction is carried out through the established time dependence model. The method has the beneficial effects that through fusion of time sequence analysis and a neural network technology, the capability of predicting the risk of the complex energy policy is remarkably improved, so that the risk management is more scientific, and the decision is more flexible. The method not only provides accurate risk assessment for the energy field, but also can be applied to a plurality of industries such as finance and environmental protection in a cross-boundary manner, and updates data in real time to cope with market fluctuation, thereby optimizing policy making and enterprise strategic planning.

Description

Technical Field

[0001] The present invention relates to the technical field of energy risk analysis, and in particular to an energy risk analysis method and system based on a time-dependent model. Background Art

[0002] The formulation and implementation of energy policies, to a certain extent, is a kind of prediction and control of the future energy market and energy environment. Therefore, the risk analysis of energy policies, that is, the prediction and evaluation of the feasibility of policy goals, the identification, evaluation and response to the risks that policies may face, can improve the scientific nature of policy formulation and help manage and control risks during policy implementation.

[0003] However, there are many risk factors in energy policy, including macroeconomic environment, energy market conditions, technological progress, environmental regulations, public awareness, etc. These factors interact with each other, showing complex dynamics and nonlinearity, making risk analysis very difficult. Traditional risk analysis methods, such as sensitivity analysis, scenario analysis, Monte Carlo simulation, etc., often assume the independence and linear effects of risk factors, and do not adequately process time series information, and cannot meet the needs of complex energy policy risk analysis.

[0004] In addition, traditional risk analysis methods are usually used for risk analysis of a single policy, while it is a challenge to deal with the risk interaction effects of multiple policy combinations. How to build a realistic and complex energy policy risk analysis model to comprehensively consider various risk factors and thus conduct effective risk management and control is an important issue in current energy policy research. Summary of the invention

[0005] In view of the above problems or problems existing in the prior art, the present invention is proposed.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides the following technical solutions: an energy risk analysis method based on a time-dependent model, comprising defining a risk metric and obtaining relevant data affecting the risk metric;

[0008] Establish a time-dependency model based on the acquired relevant data;

[0009] Risk prediction is performed through the established time-dependent model.

[0010] As a preferred solution of the energy risk analysis method based on the time-dependent model of the present invention, defining risk metrics includes determining risk events and quantifying risks.

[0011] As a preferred solution of the energy risk analysis method based on the time-dependent model of the present invention, the risk metrics include but are not limited to energy price fluctuations, energy supply interruptions and energy project failures.

[0012] As a preferred solution of the energy risk analysis method based on the time-dependency model of the present invention, the time-dependency model combines the neural network model with the autoregressive integrated moving average model (ARIMA).

[0013] As a preferred solution of the energy risk analysis method based on the time-dependent model of the present invention, the relevant data includes but is not limited to energy prices, energy supply conditions and energy project data.

[0014] As a preferred solution of the energy risk analysis method based on the time-dependency model of the present invention, the time-dependency model is established, including:

[0015] Establish an ARIMA model, expressed as ARIMA(p,d,q);

[0016] Predict the residuals for each data point in the series according to the ARIMA model;

[0017] Establish a neural network model, where the input layer is the prediction residual and the output layer is the correction value;

[0018] The predicted values ​​of the ARIMA model and the corrected values ​​of the neural network model are combined to obtain the target prediction results.

[0019] As a preferred solution of the energy risk analysis method based on the time-dependent model of the present invention, risk prediction includes converting the prediction results into corresponding risk metrics, and integrating various risk metrics to obtain an overall risk metric.

[0020] As a preferred solution of the energy risk analysis method based on the time-dependency model of the present invention, after risk prediction is performed through the established time-dependency model, it also includes generating risk metrics and risk reports.

[0021] In a second aspect, the present invention provides the following technical solutions: an energy risk analysis system based on a time-dependent model, comprising a definition acquisition module for defining risk metrics and acquiring relevant data affecting the risk metrics;

[0022] Establishing a module for establishing a time-dependent model based on the acquired relevant data;

[0023] The prediction module is used to predict risks through the established time-dependent model.

[0024] As a preferred solution of the energy risk analysis system based on the time-dependency model of the present invention, wherein: a generation module is used to generate risk metrics and risk reports.

[0025] Beneficial effects of the invention: The invention significantly improves the ability to predict complex energy policy risks by integrating time series analysis and neural network technology, making risk management more scientific and decision-making more flexible. It not only provides accurate risk assessment for the energy field, but can also be applied across multiple industries such as finance and environmental protection, updating data in real time to cope with market fluctuations, thereby optimizing policy formulation and corporate strategic planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0027] Figure 1 Schematic diagram of the process of energy risk analysis method based on time-dependent model.

[0028] Figure 2 Schematic diagram of the process of establishing the time-dependent model. DETAILED DESCRIPTION

[0029] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0030] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0031] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0032] Example 1

[0033] Reference Figure 1-2 , which is the first embodiment of the present invention, and which provides an energy risk analysis method based on a time-dependent model, comprising defining a risk metric and obtaining relevant data affecting the risk metric;

[0034] Further, risk measures include, but are not limited to, energy price fluctuations, energy supply disruptions, and energy project failures.

[0035] Further, defining risk metrics includes identifying risk events and quantifying risks.

[0036] It should be noted that determining risk events requires a large amount of historical data and an understanding of the industry. For example, in energy policy risk analysis, risk events include: price fluctuations exceeding a certain threshold or frequency, energy supply failing to meet demand, energy project failure, etc.

[0037] It should be further explained that quantified risk is to quantify the identified risk events into a measurable value. We can further use historical data to calculate the frequency of these events and the economic losses they may bring as risk measurement targets, or establish models to predict the impact of these risk events and possible economic losses. Specifically, for energy price fluctuations, the risk measurement method is volatility. Volatility is defined as the standard deviation of asset price returns, which can reflect price fluctuations. Its calculation formula is:

[0038]

[0039] Among them, P t is the price at time t, and T is the number of returns.

[0040] For energy supply interruptions, the mean time to repair (MTTR) and mean time between failures (MTBF) can be measured, and the ratio of the two can be used to obtain the energy interruption rate.

[0041] For energy project failures, indicators such as the frequency and size of project delays (percentage of deviation from the original plan), as well as additional costs, can be considered.

[0042] Furthermore, relevant data include but are not limited to energy prices, energy supply conditions and energy project data.

[0043] It should be noted that energy price data, including historical and current prices of various energy sources (such as oil, natural gas, wind power, solar energy, etc.), can be obtained from market exchanges, research reports, etc.; energy supply data, including historical and current information on energy production, imports, exports, etc., can be obtained from government departments, energy company reports, etc.; energy project data, including project scale, cost, progress, benefits, etc., can be obtained from energy company announcements, annual reports, news reports, etc. According to the determined data sources, the above data are collected using automated tools (crawlers) or manual methods. Ensure that the collected data has a certain depth of time span to reflect historical trends and cyclical changes.

[0044] It should be further noted that the data obtained needs to be verified for accuracy. If errors or invalid data are found, they need to be collected again. In addition, the data needs to be organized and converted according to the same data format and measurement unit to ensure comparability between data.

[0045] A time-dependent model is established according to the acquired relevant data, wherein the time-dependent model is a mixed type model;

[0046] Furthermore, the time-dependent model combines the neural network model with the autoregressive integrated moving average model (ARIMA).

[0047] The neural network model performs well in processing nonlinear time series data, while the autoregressive integrated moving average model is also very effective in predicting the linear part and eliminating trend and seasonal fluctuations. Combining the two can better cope with various complex situations in energy policy risk analysis.

[0048] Furthermore, the time-dependent model is established including:

[0049] Establish an ARIMA model, expressed as ARIMA(p,d,q), where the input of the ARIMA model is the collected time series data such as energy prices, energy supply conditions and energy project data. After these data are processed by the ARIMA model, preliminary forecast results (such as preliminary forecasts of future energy prices, supply conditions and project progress) and residuals will be generated;

[0050] It should be noted that the mathematical expression of the ARIMA model is:

[0051] (1-d)L d Y t =c+Φ(L)ε t -Θ(L)ε{t-1}

[0052] Among them, L is the lag operator, Φ(L) and Θ(L) are the polynomials of the autoregressive part and the moving average part respectively, d is the difference order, and ε is the white noise sequence. First, the order of p, d, and q must be determined. This step is usually selected by the graphical method of the autocorrelation function (ACF) and the partial autocorrelation function (PACF). After calculating these indicators, the maximum likelihood estimation method can be used to estimate the parameters.

[0053] Predict the residuals for each data point in the series according to the ARIMA model;

[0054] It should be noted that the prediction residuals:

[0055] in, is the predicted value of the ARIMA model at time t.

[0056] Establish a neural network model, where the input layer is the prediction residual and the output layer is the correction value;

[0057] It should be noted that the number of hidden layer nodes h and the activation function as well as the architecture of the neural network need to be determined according to the specific situation. Then the training set is used for training to find the optimal weight vector.

[0058] It should be further explained that the input of the neural network model is the residual data obtained by the ARIMA model. These residual data reflect the part that the ARIMA model cannot predict, usually the nonlinear part. In addition, the neural network model will further correct and predict the residuals, and output these corrected values.

[0059] The predicted values ​​of the ARIMA model and the corrected values ​​of the neural network model are combined to obtain the target prediction results.

[0060] It should be noted that the ARIMA model is used to calculate the preliminary forecast value Based on the calculated residual, the correction value e is obtained through the neural network model correction. nn , and finally get the final predicted value:

[0061]

[0062] This prediction result will be used for subsequent risk measurement and strategy formulation.

[0063] Risk prediction is performed through the established time-dependent model.

[0064] Furthermore, risk prediction includes converting the prediction results into corresponding risk metrics and integrating various risk metrics to obtain an overall risk metric.

[0065] Furthermore, after risk prediction is performed using the established time-dependent model, risk metrics and risk reports are generated.

[0066] It should be noted that generating risk metrics is to convert the predicted results into corresponding risk metrics, such as converting the predicted energy price into price volatility risk, converting the predicted energy supply situation into supply interruption risk, and converting the predicted energy project situation into project failure risk. Then, the various risk metrics are combined to generate an overall risk metric. The risk report is to visualize the risk metric results and compile them into a risk report. The report should include the following: detailed information on each risk metric result, explanation of the prediction results, prediction of future risk development trends, and risk prevention and control recommendations.

[0067] In summary, the beneficial effect of the energy risk analysis method based on the time-dependent model of the present invention is that by integrating time series analysis and neural network technology, the prediction ability of complex energy policy risks is significantly improved, making risk management more scientific and decision-making more flexible. It not only provides accurate risk assessment for the energy field, but also can be applied across multiple industries such as finance and environmental protection, updating data in real time to cope with market fluctuations, thereby optimizing policy formulation and corporate strategic planning.

[0068] Example 2

[0069] This embodiment provides an energy risk analysis system based on a time-dependency model, which includes a definition acquisition module for defining risk metrics and acquiring relevant data affecting the risk metrics;

[0070] Establishing a module for establishing a time-dependent model based on the acquired relevant data;

[0071] The prediction module is used to predict risks through the established time-dependent model.

[0072] Furthermore, a generation module is provided for generating risk metrics and risk reports.

[0073] The above-mentioned unit modules may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above-mentioned modules.

[0074] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An energy risk analysis method based on a time-dependent model, characterized in that: include, Define risk metrics and obtain relevant data that affects risk metrics; Establishing a time-dependent model according to the acquired relevant data; Risk prediction is performed using the established time-dependent model.

2. The energy risk analysis method based on the time-dependent model according to claim 1, characterized in that: Defining risk metrics includes determining risk events and quantifying risks.

3. The energy risk analysis method based on the time-dependent model according to claim 2, characterized in that: The risk metrics include, but are not limited to, energy price fluctuations, energy supply disruptions, and energy project failures.

4. The energy risk analysis method based on the time-dependent model according to claim 3, characterized in that: The time-dependent model combines a neural network model with an autoregressive integrated moving average model (ARIMA).

5. The energy risk analysis method based on the time-dependent model according to claim 4, characterized in that: The relevant data include but are not limited to energy prices, energy supply conditions and energy project data.

6. The energy risk analysis method based on the time-dependent model according to claim 5, characterized in that: The time-dependent modeling includes: Establish an ARIMA model, expressed as ARIMA(p,d,q); Predict the residual for each data point in the sequence according to the ARIMA model; Establish a neural network model, where the input layer is the prediction residual and the output layer is the correction value; The predicted value of the ARIMA model and the corrected value of the neural network model are combined to obtain the target prediction result.

7. The energy risk analysis method based on the time-dependent model according to claim 6, characterized in that: The risk prediction includes converting the prediction results into corresponding risk metrics, and integrating various risk metrics to obtain an overall risk metric.

8. The energy risk analysis method based on the time-dependent model according to any one of claims 1 to 7, characterized in that: After the risk prediction is performed by using the established time-dependent model, the method further includes generating risk metrics and risk reports.

9. An energy risk analysis system based on a time-dependent model, characterized in that: Definition acquisition module, used to define risk measurement and obtain relevant data affecting risk measurement; An establishment module is used to establish a time-dependent model according to the acquired relevant data; The prediction module is used to perform risk prediction through the established time-dependent model.

10. The energy risk analysis system based on the time-dependent model according to claim 9, characterized in that: The generation module is used to generate risk metrics and risk reports.

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