Management method, system and equipment for energy conservation prediction and medium

Abstract

The invention discloses a management method, system and equipment for energy conservation prediction and a medium, and relates to the technical field of energy conservation optimization, and the method comprises the steps: collecting multi-source data of an energy use terminal, and carrying out the preprocessing of the multi-source data; and performing feature extraction and data analysis on the preprocessed data, and performing energy consumption prediction based on a deep neural network model. And generating an energy-saving strategy according to a prediction result, and performing dynamic optimization adjustment through a feedback mechanism. According to the method provided by the invention, the feature weight is adaptively adjusted through the L-VAE, the model calculation complexity is reduced, and the feature representation capability is improved, so that the energy consumption prediction can still keep efficient operation under the conditions of large data volume and complex features, and information loss is avoided. The method improves the adaptability of energy consumption prediction, enables the prediction result to be rapidly adjusted in the case of sudden load change, guarantees the prediction accuracy, and supports the making of a finer energy-saving strategy.

Description

Technical Field

[0001] The present invention relates to the technical field of energy saving optimization, and in particular to a management method, system, equipment and medium for energy saving prediction. Background Art

[0002] Energy conservation management has become an important research direction in the fields of industry, commerce and public infrastructure. Traditional energy management methods mainly rely on rule-based strategy formulation, such as fixed-time scheduling and equipment energy consumption threshold control, but these methods are difficult to adapt to complex changes in energy demand. In recent years, data-driven energy forecasting technology has developed rapidly, especially energy consumption forecasting models based on machine learning and deep learning, which can analyze future energy consumption trends through historical data and improve the intelligence level of energy conservation management. In addition, the application of reinforcement learning in energy optimization decision-making has gradually attracted attention. By constructing a state-action-reward mechanism, it can dynamically optimize energy allocation strategies and improve energy utilization efficiency. However, the existing technology still has problems such as large data noise interference, insufficient feature extraction, and difficult to predict short-term load fluctuations, which limits the real-time and accuracy of energy-saving strategies. Therefore, how to efficiently and accurately predict energy consumption and optimize energy-saving strategies based on the prediction results has become an important technical challenge in the field of energy management.

[0003] At present, energy consumption forecasting and energy-saving optimization mainly rely on traditional time series analysis methods (such as ARIMA) and neural network-based forecasting models (such as LSTM, GRU), but these methods have the following limitations when facing the dynamic characteristics of complex energy systems:

[0004] Insufficient feature selection and dimensionality reduction: Existing methods usually use principal component analysis (PCA) or simple statistical screening methods to extract features from energy consumption data, but it is difficult to capture the correlation between nonlinear features, resulting in the model input data dimension being too high, affecting the prediction accuracy.

[0005] Limited ability to predict short-term energy consumption fluctuations: Traditional deep learning methods, such as LSTM or GRU, have certain advantages in time series modeling, but have a lag effect when dealing with drastic fluctuations in short-term energy consumption, resulting in large prediction errors.

[0006] Energy consumption optimization strategies lack adaptability: Most existing energy-saving optimization methods rely on fixed rules or simple optimization methods, which make it difficult to adjust strategies in real time and unable to adapt to environmental changes (such as load changes, weather effects, etc.), resulting in poor execution of energy-saving strategies.

[0007] Imperfect feedback mechanism: Many existing systems do not have a closed-loop feedback mechanism. After the energy-saving strategy is implemented, it is impossible to dynamically optimize it according to the actual energy consumption situation, making it difficult to continuously improve the strategy optimization process and reducing the long-term energy-saving effect. Summary of the Invention

[0008] In view of the above problems, the present invention is proposed.

[0009] Therefore, the technical problems solved by the present invention are: existing energy consumption prediction and management methods have problems such as insufficient feature selection, limited short-term energy consumption fluctuation prediction ability, lack of self-adaptability of energy-saving strategies, and imperfect feedback mechanisms, as well as the problem of how to construct a closed-loop energy-saving management system to achieve accurate energy consumption prediction and dynamic optimization of energy-saving control.

[0010] To solve the above technical problems, the present invention provides the following technical solutions: A management method for energy-saving prediction includes: collecting multi-source data of energy usage terminals and performing preprocessing. Performing feature extraction and data analysis on the preprocessed data, and performing energy consumption prediction based on a deep neural network model. Generating an energy-saving strategy according to the prediction result and performing dynamic optimization and adjustment through a feedback mechanism. The deep neural network model includes an input layer, a variational autoencoder layer, a time series modeling layer, a Transformer prediction layer, a fully connected layer, and an output layer. The input layer receives the feature set that affects the campus energy consumption prediction and embeds it into a high-dimensional feature space through a multi-head self-attention mechanism. The variational autoencoder layer introduces a Laplace variational autoencoder to perform feature dimensionality reduction on the input campus energy consumption data, remove redundant information, optimize data representation, regularize the latent variables output by the encoder using a Laplace prior distribution, optimize the variational lower bound through Laplace variational inference, and generate a low-dimensional interpretable feature representation by balancing the decoder reconstruction error and the KL divergence. The time series modeling layer models the short-term changes of energy consumption data through a gated recurrent unit and dynamically adjusts the hidden state in combination with an attention mechanism. The Transformer prediction layer adopts a local spatio-temporal attention mechanism, adds local time window regulation to the global Transformer architecture, dynamically divides the input sequence based on a sliding time window, applies local attention weights to the time steps within the window, adjusts the contribution ratio of global and local attention through a time weighting mechanism, and optimizes the joint modeling ability for short-term energy consumption fluctuations and long-term trends. The fully connected layer processes the temporal features output by the Transformer layer and uses the ReLU activation function to enhance the non-linear mapping ability. The output layer converts the output of the fully connected layer into the final energy consumption prediction result.

[0011] As a preferred embodiment of the management method for energy-saving prediction of the present invention, wherein: the preprocessing includes: collecting campus energy consumption data through energy usage terminals and performing time series alignment on the data based on a time synchronization mechanism. Cleaning the campus energy consumption data after time series alignment, including removing duplicate data, abnormal data, and filling in missing data.

[0012] As a preferred solution of the energy conservation prediction management method described in the present invention, wherein: the feature extraction and data analysis include: segmenting the preprocessed campus energy consumption data by time series, adopting a feature selection method based on multi-layer nested clustering, classifying and summarizing the global pattern and local pattern of the campus energy consumption data respectively, and calculating the mutual influence degree between feature variables by combining mutual information analysis, so as to screen the feature set that has the most significant influence on the campus energy consumption prediction.

[0013] As a preferred solution of the energy conservation prediction management method described in the present invention, wherein: the deep neural network model includes: an input layer receives the feature set that has the most significant influence on the campus energy consumption prediction, and adopts a multi-head self-attention embedding mechanism to map multi-dimensional input data to a high-dimensional feature space. A variational autoencoder layer introduces a Laplace variational autoencoder to perform feature dimensionality reduction on the input campus energy consumption data, removes redundant information, optimizes data representation, regularizes the latent variables output by the encoder by using a Laplace prior distribution, optimizes the variational lower bound through Laplace variational inference, optimizes the modeling ability for the sparsity and mutation characteristics of energy consumption data, and combines the reconstruction error of the decoder and the KL divergence balance to generate a low-dimensional interpretable feature representation. A time series modeling layer uses a gated recurrent unit for time series modeling to learn the short-term and long-term energy consumption change trends from the input time series energy consumption data. The hidden state weights of the gated recurrent unit are dynamically adjusted through an attention mechanism. A Transformer prediction layer adopts a local spatio-temporal attention mechanism, adds local time window regulation on the global Transformer architecture, dynamically divides the input sequence based on a sliding time window, applies local attention weights to the time steps within the window, and adjusts the contribution ratio of global and local attention through a time weighting mechanism to optimize the joint modeling ability for short-term energy consumption fluctuations and long-term trends. A fully connected layer receives the time series features output by the Transformer prediction layer and performs a non-linear transformation, and uses a ReLU activation function to enhance the non-linear fitting ability. An output layer converts the output of the fully connected layer into the final energy consumption prediction result.

[0014] As a preferred solution of the energy-saving prediction management method described in the present invention, wherein: the energy consumption prediction includes: standardizing the feature set that has the most significant impact on the energy consumption prediction of the park, and converting it to the same scale by using the Z-score normalization method. Using an embedding layer based on multi-head self-attention to input the feature set data of the same scale into a deep neural network to capture the long-term and short-term dependencies between different features. Based on a variational autoencoder for feature dimensionality reduction, converting the features into low-dimensional latent vector representations, and introducing a gated recurrent unit GRU for time series modeling to extract the temporal features of energy consumption changes. Inputting the output result of the GRU into a fully connected layer, and using the Softmax activation function to calculate the probability distribution of the park's energy consumption at different time steps, and combining Bayesian regression model calculation to obtain the final park energy consumption prediction result.

[0015] As a preferred solution of the energy-saving prediction management method described in the present invention, wherein: generating an energy-saving strategy based on the prediction result includes: calculating and generating an energy-saving strategy for the park's energy consumption according to the park's energy consumption prediction result, using a reinforcement learning algorithm to evaluate the adaptability of different strategies, and screening the optimal energy-saving strategy.

[0016] As a preferred solution of the energy-saving prediction management method described in the present invention, wherein: and performing dynamic optimization and adjustment through a feedback mechanism includes: executing the optimal energy-saving strategy, continuously monitoring the actual energy consumption data, obtaining the real-time device operating status, environmental parameters and load levels, and forming a feedback data set of the execution effect of the energy-saving strategy. Based on the feedback data set, using an adaptive error analysis model to calculate the error between the actual energy consumption value and the park's energy consumption prediction result, and analyzing the error source. According to the error analysis result, dynamically optimize the energy-saving strategy.

[0017] Another object of the present invention is to provide an energy-saving prediction management system, which can perform feature extraction and data analysis on preprocessed data, and perform energy consumption prediction based on a deep neural network model, solving the problem that traditional deep learning methods (LSTM or GRU) have a lag effect when dealing with drastic short-term energy consumption fluctuations, resulting in large prediction errors.

[0018] As a preferred solution of the energy-saving prediction management system described in the present invention, wherein: it includes a preprocessing module, a prediction module, and an optimization module.

[0019] The preprocessing module is used to collect multi-source data of energy usage terminals and perform preprocessing.

[0020] The prediction module is used to perform feature extraction and data analysis on the preprocessed data, and perform energy consumption prediction based on a deep neural network model.

[0021] The optimization module is used to generate an energy-saving strategy based on the prediction result and perform dynamic optimization and adjustment through a feedback mechanism.

[0022] A computer device includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of a management method for energy-saving prediction.

[0023] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the steps of a management method for energy-saving prediction.

[0024] Advantages of the present invention: The management method for energy-saving prediction provided by the present invention uses energy consumption terminals in the park to collect energy consumption data, performs time series alignment on the data through a time synchronization mechanism, and simultaneously performs data cleaning, including removing duplicate data and abnormal data, and filling missing data using interpolation. This ensures the quality of the subsequent model training data, enables the energy consumption prediction model to learn on a stable and clean data set, improves the prediction accuracy, and reduces errors.

[0025] The preprocessed park energy consumption data is segmented by time series. A feature selection method based on multi-layer nested clustering is used to classify and summarize the global pattern and the local pattern respectively, and the mutual information analysis is combined to calculate the mutual influence degree between feature variables, and the feature set that has the most significant impact on energy consumption prediction is screened out. This step makes the extraction of energy consumption characteristics more targeted by classifying and summarizing the global and local patterns. At the same time, the key variables are accurately screened with the help of mutual information analysis, avoiding the influence of redundant features on the prediction accuracy. It improves the interpretability of energy consumption data, reduces the noise brought by irrelevant features, enables the prediction model to learn the energy consumption law more efficiently, improves the generalization ability, and can still maintain a high prediction accuracy in different park environments.

[0026] The variational autoencoder layer introduces the Laplace variational autoencoder (L-VAE) to automatically capture the structural information of high-dimensional data and uses Laplace prior regularization encoding to make the low-dimensional representation more interpretable. This step adaptively adjusts the feature weights through L-VAE, making the dimensionality reduction process of energy consumption data more accurate, which can not only reduce data redundancy but also retain the most critical information for prediction. It reduces the model calculation complexity, improves the feature representation ability, enables the energy consumption prediction to still operate efficiently in the case of large data volume and complex features, and avoids information loss.

[0027] The time series modeling layer uses a gated recurrent unit (GRU) for time series modeling, learns short-term and long-term energy consumption change trends from the input time series energy consumption data, and dynamically adjusts the hidden state weights of the GRU through an attention mechanism. The Transformer prediction layer introduces a local spatio-temporal attention mechanism (LSTA). Based on the global self-attention calculation, combined with the adjustment of local time window weights, it enhances the modeling ability for short-term fluctuations. This step combines the long-term dependence ability of the GRU and the global modeling ability of the Transformer, and enhances the ability to capture short-term energy consumption changes through the LSTA, enabling energy consumption prediction to consider both long-term trends and make rapid responses to short-term load changes. It improves the adaptability of energy consumption prediction, enabling the system to quickly adjust the prediction results in the face of sudden load changes, ensuring the prediction accuracy, and thus supporting the formulation of more refined energy-saving strategies.

[0028] The present invention constructs a complete closed-loop energy-saving prediction management method. Compared with traditional methods, our invention has significant advantages in data dimensionality reduction, short-term energy consumption prediction, energy-saving strategy optimization and dynamic adjustment, making energy consumption prediction more accurate and energy-saving strategies more efficient. Brief Description of the Drawings

[0029] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for description in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0030] Figure 1 It is the overall flowchart of a management method for energy-saving prediction provided by the first embodiment of the present invention. Detailed Embodiments

[0031] To make the above objects, features, and advantages of the present invention more obvious and understandable, the specific embodiments of the present invention will be described in detail below with reference to the drawings of the specification. Obviously, the described embodiments are some embodiments of the present invention, rather than all 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 scope of protection of the present invention.

[0032] Example 1, referring to Figure 1 , which is an embodiment of the present invention, provides a management method for energy-saving prediction, including: S1: Collect multi-source data of energy usage terminals and perform preprocessing.

[0033] Collect the energy consumption data of the park through the energy - using terminals, and align the data in time series based on the time - synchronization mechanism. Clean the energy consumption data of the park after time - series alignment, including removing duplicate data, abnormal data, and filling in missing data.

[0034] It should be noted that a preferred solution for collecting the energy consumption data of the park through the energy - using terminals specifically includes that the system collects power consumption data, environmental parameter data, load and equipment status data, and user behavior data through the energy - using terminals (including smart meters, smart air - conditioner controllers, lighting management systems, temperature and humidity sensors, etc.) installed in each energy - using unit of the park. The power consumption data includes total active power, voltage, current, power factor, and sub - circuit energy consumption data. The environmental parameter data includes outdoor environmental temperature, humidity, solar radiation intensity, indoor temperature and humidity. The load and equipment status data includes equipment start - stop status, running time, and load change rate. The user behavior data includes the flow data of office workers and the working day / holiday mark.

[0035] Furthermore, a preferred solution for aligning the data in time series based on the time - synchronization mechanism specifically includes that since the data comes from multiple terminal devices, there may be clock deviations in their collection times. Therefore, the Network Time Protocol (NTP) is used for time synchronization to ensure that all data is stored and calculated based on the same time reference. Linear interpolation is used to align the data collected at different times to a fixed time interval (such as one data point every 5 minutes). For some data points with discontinuous time series, local weighted regression (LOWESS) is used for smoothing to reduce the impact of mutation points.

[0036] Even further, a preferred solution for removing duplicate data, abnormal data, and filling in missing data specifically includes removing duplicate data by de - duplicating based on the device ID and timestamp to ensure that there is only one piece of data at each time point. The Z - score method (with a threshold set to 3σ) is used for outlier detection and elimination to detect abnormal - fluctuation power data, such as suddenly emerging extreme high - load situations, and eliminate the outlier points beyond 3 times the standard deviation. Numerically impossible values are directly eliminated. For missing values in data collection, for short - term missing values (<1 hour), linear interpolation is used for filling to ensure the smoothness of the data in a short period. For long - term missing values (>1 hour), Long Short - Term Memory (LSTM) time - series prediction is used to fill in the long - term data missing to avoid the impact of data fluctuations on model training.

[0037] It should be noted that the core goal of S1 is to obtain high-quality park energy consumption data to ensure the accuracy of subsequent energy consumption prediction and the rationality of energy-saving strategies. The design idea is to improve the integrity, consistency, and timeliness of data through multi-source data collection and preprocessing methods such as time synchronization, data cleaning, anomaly detection, and missing value filling. The effect of such design is to eliminate data errors, improve data availability, and make the data input into the energy consumption prediction model more representative. For example, time synchronization can ensure that data from different devices is aligned under the same time reference, data cleaning can remove invalid or abnormal data, and missing value filling can reduce the bias in model training. And the reason for adopting this design is that the energy usage in the park is time-varying, complex, and diverse, the energy consumption characteristics of different types of devices are different, and the data may be affected by factors such as sensor failures, network fluctuations, and equipment aging. Without effective preprocessing, it will lead to too much noise in the data input into the prediction model, affecting the accuracy of energy consumption prediction. Therefore, reasonable data collection and preprocessing are the key links to ensure the effectiveness of subsequent prediction and optimization decisions.

[0038] S2: Extract features and conduct data analysis on the preprocessed data, and perform energy consumption prediction based on a deep neural network model.

[0039] Segment the time series of the preprocessed park energy consumption data, adopt a feature selection method based on multi-layer nested clustering, classify and summarize the global patterns and local patterns of the park energy consumption data respectively, and calculate the mutual influence degree between feature variables by combining mutual information analysis to screen the feature set that has the most significant impact on park energy consumption prediction.

[0040] A preferred solution for classifying and summarizing the global patterns and local patterns of the park energy consumption data specifically includes, in each time window dataset, using hierarchical clustering to divide the global pattern and calculating data similarity using Euclidean distance. On the basis of hierarchical clustering, nested density clustering is used to further refine the classification of local time windows to ensure that sudden events such as temporary start and stop of equipment and sudden load increase can be accurately captured in the short-term energy consumption characteristics.

[0041] A preferred solution for screening the feature set that has the most significant impact on park energy consumption prediction specifically includes calculating the dependence relationship between different energy consumption variables using mutual information entropy. Set the information threshold to 0.05. If the mutual information between a certain feature and the target variable (future energy consumption) is lower than this threshold, then this feature is eliminated. According to the calculation result of mutual information, select the top 10 variables with the highest mutual information values as the core features, and use principal component analysis for further dimensionality reduction to screen the principal components that can explain more than 90% of the variance as the final feature input into the deep neural network model.

[0042] The deep neural network model includes an input layer that receives the feature set with the most significant impact on the campus energy consumption prediction. It adopts a multi-head self-attention embedding mechanism to map multi-dimensional input data into a high-dimensional feature space. The variational autoencoder layer introduces a Laplace variational autoencoder to perform feature dimensionality reduction on the input campus energy consumption data, remove redundant information, optimize data representation, regularize the latent variables output by the encoder using a Laplace prior distribution, optimize the variational lower bound through Laplace variational inference, enhance the modeling ability for the sparsity and mutation characteristics of energy consumption data, and balance the reconstruction error of the decoder and the KL divergence to generate low-dimensional interpretable feature representations. The time series modeling layer uses a gated recurrent unit for time series modeling to learn the short-term and long-term energy consumption change trends from the input time series energy consumption data. The hidden state weights of the gated recurrent unit are dynamically adjusted through an attention mechanism. The Transformer prediction layer adopts a local spatio-temporal attention mechanism, adds local time window regulation to the global Transformer architecture, dynamically divides the input sequence based on a sliding time window, applies local attention weights to the time steps within the window, and adjusts the contribution ratio of global and local attention through a time weighting mechanism to enhance the joint modeling ability for short-term energy consumption fluctuations and long-term trends. The fully connected layer receives the time series features output by the Transformer prediction layer and performs a non-linear transformation, using the ReLU activation function to enhance the non-linear fitting ability. The output layer converts the output of the fully connected layer into the final energy consumption prediction result.

[0043] Furthermore, a preferred solution of the variational autoencoder layer specifically includes that in the variational autoencoder (VAE) layer, a Laplace variational autoencoder (L-VAE) is introduced to enhance the robustness in the data dimensionality reduction process and reduce non-linear noise interference. The standard VAE uses a Gaussian distribution as the prior distribution, which is difficult to effectively model sparse data and energy consumption mutation data. However, the invention of our side adopts a Laplace prior distribution (LaplacePrior), which is more suitable for the sparse characteristics of energy consumption data and can more effectively remove irrelevant information. It is expressed as: ; Where, represents the loss function of the Laplace variational autoencoder. represents the mathematical expectation under the variational distribution and is used to calculate the average log-likelihood value of the reconstructed data. represents the input high-dimensional energy consumption feature matrix. represents the low-dimensional latent variable. represents the variational distribution. represents the decoder generation distribution. denotes the Kullback-Leibler divergence, which is used to measure the similarity between the variational distribution and the prior distribution. denotes the reconstruction loss term, which is used to measure the input data and the reconstructed by the latent variables, ensuring that the features after dimensionality reduction can retain the information of the original data to the greatest extent. denotes measuring the distribution of the latent variables and the prior distribution to make the features after dimensionality reduction more sparse and stable. denotes the weight of the KL divergence, with a value , which is used to balance the influence of the reconstruction error and the prior matching. Adopt the Laplace distribution: ; where denotes the mean, denotes the scale parameter, taking to maintain data sparsity.

[0044] Furthermore, a preferred solution of the Transformer prediction layer specifically includes introducing a local spatio-temporal attention mechanism (Local Spatio-Temporal Attention, LSTA) in the Transformer prediction layer to enhance the model's sensitivity to short-term fluctuations while maintaining the long-term trend modeling ability. The standard Transformer uses global self-attention calculation, ignoring the contribution of the local time window to the prediction result, resulting in difficulty in modeling short-term burst loads. And the computational complexity is relatively high, consuming too much computing resources in the energy consumption prediction scenario. Adopt local window selection (Sliding Time Window Selection) to dynamically adjust the window size and improve the modeling ability for short-term load changes. Combine temporal weighted attention (Temporal Weighted Attention) to enhance the adaptability of the Transformer to different time steps. It is expressed as: ; where denotes the attention weight within the local time window. denotes calculating the correlation score of the local time window. denotes calculating the correlation score of the local time window at time step . denotes the time window size, set to ensure that the model can adapt to the needs of different time scales. Represents the mapping matrix of the query, Represents the mapping matrix of the key. Represents the time step of the query vector, Represents the time step of the key vector to ensure the correlation calculation of time series data. Represents the transpose of the weight matrix. .8, then the short-term burst load has a greater impact on the prediction. If .2, then it mainly relies on long-term trend modeling.

[0045] The energy consumption prediction steps are as follows:

[0046] Standardize the feature set that has the most significant impact on the park energy consumption prediction, and convert it to the same scale using the Z-score normalization method. Input the feature set data of the same scale into a deep neural network using an embedding layer based on multi-head self-attention to capture the long-term and short-term dependencies between different features. Perform feature dimensionality reduction based on a variational autoencoder, convert the features into low-dimensional latent vector representations, and introduce a gated recurrent unit GRU for time series modeling to extract the temporal features of energy consumption changes. Input the output result of the GRU into a fully connected layer, and use the Softmax activation function to calculate the probability distribution of the park energy consumption at different time steps. Combine the calculation of the Bayesian regression model to obtain the final park energy consumption prediction result.

[0047] It should be noted that the core objective of S2 is to extract key features from the preprocessed energy consumption data and perform energy consumption prediction through a deep neural network model to ensure the accuracy and generalization ability of the prediction. The design idea is to use multi-layer nested clustering, mutual information analysis, and principal component analysis to screen the most important features, and combine with the Laplace variational autoencoder (L-VAE) for dimensionality reduction to remove redundant information. Then, a local spatio-temporal attention mechanism (LSTA) is used to optimize the Transformer to improve the modeling ability of short-term load fluctuations, so as to enhance the accuracy and adaptability of time series prediction. This step ensures that the feature data input into the deep learning model is the most representative through multi-layer clustering and mutual information analysis, and avoids the influence of redundant data on the prediction results. The L-VAE is used to reduce the dimensionality of the energy consumption features, making the data representation more sparse and improving the stability of model training. The Transformer is optimized by the LSTA, enabling the model to take into account both short-term fluctuations and long-term trends, and improving the accuracy of energy consumption prediction. Because traditional feature selection methods (such as PCA) cannot effectively distinguish short-term abnormal energy consumption fluctuations, while multi-layer nested clustering can refine short-term and long-term patterns and improve the adaptability of feature extraction. The Gaussian prior distribution of the traditional VAE is difficult to effectively model the sparse characteristics of energy consumption data, while the L-VAE can enhance the feature sparsification ability and improve the dimensionality reduction effect. The traditional Transformer has a high computational complexity and is difficult to handle short-term energy consumption fluctuations. The LSTA adds a local time window on the basis of global attention, enabling the model to more effectively learn the impact of sudden loads and improving the robustness of the prediction.

[0048] S3: Generate energy-saving strategies based on the prediction results and perform dynamic optimization and adjustment through a feedback mechanism.

[0049] Calculate and generate the energy-saving strategy for the park's energy consumption according to the park's energy consumption prediction results, use the reinforcement learning algorithm to evaluate the adaptability of different strategies, and screen the optimal energy-saving strategy. Perform dynamic optimization and adjustment through a feedback mechanism. First, execute the optimal energy-saving strategy, continuously monitor the actual energy consumption data, obtain the real-time device operation status, environmental parameters, and load level, and form a feedback data set of the execution effect of the energy-saving strategy. Based on the feedback data set, use an adaptive error analysis model to calculate the error between the actual energy consumption value and the park's energy consumption prediction result, and analyze the error source. Dynamically optimize the energy-saving strategy according to the error analysis result.

[0050] An optimal solution for evaluating the adaptability of different strategies using a reinforcement learning algorithm and screening the optimal energy-saving strategy specifically includes the following steps. In this step, based on the predicted results of the park energy consumption, an energy-saving strategy for the park energy consumption is calculated and generated, and a reinforcement learning algorithm is used to evaluate the adaptability of different strategies to screen the optimal energy-saving strategy. First, based on the energy consumption prediction results, a multi-objective optimization model is established. The optimization objectives include minimizing the energy consumption cost, reducing the peak load, and improving the equipment utilization rate. The constraint conditions cover the equipment operation time, load balance, and user comfort parameters. For this optimization problem, an intelligent decision-making framework based on reinforcement learning is adopted, including the state space, action space, reward function, policy evaluation, and policy selection. The state space contains information such as the current operation state of the park equipment, load level, and environmental factors (temperature, humidity). The action space defines possible energy-saving operations, such as adjusting the equipment operation time, optimizing the load distribution, and reducing the power consumption of unnecessary equipment. The reward function combines three factors: energy-saving effect, user comfort, and equipment life, calculates the benefits of different strategies, and uses a weighted objective function to balance multiple optimization objectives. Policy evaluation uses a deep Q-network (DQN) for reinforcement learning training and combines an experience replay mechanism to enable the policy to adapt to the changes in the park energy consumption. Policy selection is based on the results of multiple iterative trainings, and a Monte Carlo tree search (MCTS) is used to screen the optimal energy-saving strategy to ensure that the policy has generalization ability in different environments. After implementing the optimal energy-saving strategy, dynamic optimization and adjustment are carried out through a feedback mechanism, that is, the actual energy consumption data is continuously monitored, and the equipment operation state, environmental parameters, and load level are collected to form a feedback data set of the execution effect of the energy-saving strategy. Subsequently, an adaptive error analysis model is used to calculate the error between the actual energy consumption value and the predicted energy consumption value. The error sources include:

[0051] Load fluctuation error: The energy consumption deviation caused by the temporary start and stop of equipment, production plan adjustment, etc.

[0052] Environmental impact error: The error in energy consumption demand affected by factors such as weather changes and temperature and humidity changes.

[0053] Equipment operation error: The power loss deviation caused by factors such as equipment aging and untimely maintenance.

[0054] A preferred solution for dynamically optimizing the energy-saving strategy according to the error analysis results specifically includes using Bayesian optimization to adjust the reward function in the reinforcement learning model, enabling the strategy to adapt to the energy consumption patterns of different scenarios. Combining an adaptive sliding window to optimize the input data of the energy consumption prediction model, enhancing the model's response ability to short-term changes. Using multi-layer clustering analysis to reclassify different device loads and adjusting the energy-saving strategy for different categories of devices. For example, a more conservative energy-saving strategy is adopted for high-fluctuation load devices, and a more aggressive optimization strategy is adopted for stable load devices. Finally, the optimized energy-saving strategy will be re-executed in the next energy-saving cycle, and the feedback optimization process will be repeated to form a closed-loop energy-saving management system, enabling the energy-saving strategy to continuously adapt to the dynamically changing energy consumption demand and improving the overall energy utilization efficiency of the park.

[0055] It should be noted that the core goal of S3 is to formulate an energy-saving strategy based on the energy consumption prediction results and dynamically optimize it through reinforcement learning and feedback mechanism, enabling the energy-saving strategy to continuously adapt to the changing energy consumption demand. The design idea is based on a multi-objective optimization model, comprehensively considering the energy consumption cost, peak load, and equipment utilization rate, using the reinforcement learning algorithm (DQN+MCTS) to screen the optimal energy-saving strategy, and realizing the adaptive optimization of the strategy through the feedback mechanism and error analysis. This step automatically finds the optimal strategy through reinforcement learning to ensure that the energy-saving solution can operate efficiently under different load conditions. Using the feedback mechanism to continuously adjust the strategy enables the energy-saving solution to adapt to sudden load fluctuations or environmental changes and improve the energy utilization rate. Error analysis can accurately identify the deviation in strategy execution, forming a closed-loop optimization for energy-saving management and improving the long-term energy-saving effect. Since the traditional rule optimization method has poor adaptability in scenarios with large load fluctuations, reinforcement learning can dynamically adjust the strategy to improve the flexibility of the energy-saving solution. Factors such as equipment aging and environmental changes may affect the strategy execution effect, so a feedback mechanism is needed for adaptive optimization. By adjusting the reward function through Bayesian optimization, the strategy can be prevented from converging to a local optimum, improving the generalization ability of energy-saving management and making the energy consumption management of the entire park more intelligent and efficient.

[0056] Example 2, an embodiment of the present invention, provides a management method for energy-saving prediction. To verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0057] The experiment was carried out in a large industrial park, which has multiple high-energy-consuming devices, such as central air-conditioning systems, intelligent lighting systems, electric mechanical equipment, etc. The experiment used real device energy consumption data for testing and combined the multi-layer nested clustering feature selection method, Laplace variational autoencoder (L-VAE) dimensionality reduction, local spatio-temporal attention (LSTA) optimized Transformer prediction model, and energy-saving strategy optimization method based on reinforcement learning (DQN+MCTS) proposed by the invention to carry out energy consumption prediction and energy-saving control strategy formulation.

[0058] In the experiment, energy consumption data of each area were first obtained through smart meters, temperature and humidity sensors, load monitoring devices, etc., and the core parameters were recorded. All data were aligned using a time synchronization mechanism and underwent data cleaning to remove outliers to ensure data integrity.

[0059] A multi-layer nested clustering method was used to analyze the global and local energy consumption patterns, and the influence degree of key features on energy consumption was calculated through mutual information analysis, so as to screen out the most representative feature set. Then, the L-VAE model was used to reduce the dimension of the features to remove redundant information and improve the data processing efficiency. Subsequently, a Transformer prediction model combining GRU and LSTA was used for energy consumption trend prediction, where GRU was responsible for learning the long-term energy consumption change trend and LSTA improved the sensitivity to short-term fluctuations. The energy consumption data predicted by the model will be directly used for the formulation of energy-saving strategies.

[0060] Based on the energy consumption prediction results, reinforcement learning optimization (DQN+MCTS) was used to formulate energy-saving strategies, and the optimization objectives included reducing peak load, improving equipment utilization rate, and reducing the power consumption of unnecessary equipment. The system adaptively adjusted the strategy parameters so that the energy-saving strategies could adapt to the dynamic load environment in real time.

[0061] After the implementation of the energy-saving strategy, the system continuously monitored the energy consumption data of the equipment, compared the deviation between the actual energy consumption and the predicted energy consumption, and calculated the implementation effect of the strategy. An adaptive error analysis model was used to calculate the error sources, and the strategy parameters were adjusted based on Bayesian optimization to ensure the continuous optimization of energy-saving management. The experiment was compared in two time periods of high load (daytime production peak) and low load (nighttime maintenance period) to test the adaptive energy-saving ability of the system.

[0062] Table 1 records the energy consumption of different equipment during the experiment and the energy-saving effect after optimization based on the method of the present invention (both in kWh). The data compares the energy consumption of the traditional energy consumption management method and the method of the present invention.

[0063] Table 1 Experimental data table Equipment Name Traditional Management Energy Consumption (kWh) Predicted Energy Consumption (kWh) Actual Energy Consumption after Optimization (kWh) Energy Saving Ratio (%) Load Volatility Rate (%) Equipment Utilization Rate (%) Central Air Conditioning System 350 330 310 11.4 5.2 87.5 Intelligent Lighting System 120 115 108 10.0 3.8 90.1 Electric Mechanical Equipment 500 470 450 10.0 6.5 85.0 Data Center Server 600 580 560 6.7 2.1 95.2 Ventilation System 200 185 180 10.0 4.3 88.3 Standby Power Supply System 150 140 138 8.0 3.5 93.0

[0064] It can be seen from the experimental data that after adopting the energy-saving prediction management system of the present invention, the actual energy consumption of each equipment has decreased significantly, and the overall energy-saving ratio has reached 9.35% on average. Compared with the traditional method, the energy consumption prediction model of the present invention can more accurately predict future load changes, enabling the energy-saving strategy to be adapted in advance and effectively reducing unnecessary energy waste. In addition, the load volatility has been reduced by more than 25% on average, which indicates that the system not only optimizes the energy distribution, but also effectively reduces the load instability and improves the energy utilization efficiency.

[0065] In terms of equipment utilization, the optimization strategy of the method of the present invention has significantly improved the utilization of high - energy - consuming equipment (such as central air - conditioners and data - center servers). For example, the utilization rate of data - center servers has reached 95.2%, which is about 3% higher than that of the traditional management mode, proving that the energy - saving strategy of the present invention not only saves energy consumption but also optimizes the operation efficiency of equipment.

[0066] In addition, this experiment also proves that the reinforcement - learning optimization strategy proposed by the present invention can adapt to different load environments, automatically adjust the energy - consumption management method, and make the energy - saving measures more accurate. For example, in the central - air - conditioner system, after optimization by reinforcement learning, the actual energy consumption is further reduced by 6% compared with the predicted energy consumption, which is difficult to achieve by traditional fixed strategies. This system dynamically optimizes the strategy based on a feedback mechanism, combines Bayesian optimization to adjust the reward function, and continuously optimizes the energy - saving strategy during long - term operation, ensuring continuous energy - saving effects.

[0067] Generally speaking, the present invention has the following technical advantages compared with traditional methods:

[0068] Improve the accuracy of energy - consumption prediction: The LSTA mechanism enhances the perception ability of short - term load fluctuations, reducing the prediction error.

[0069] Optimize the energy - saving strategy and reduce energy waste: Reinforcement learning for adaptive optimization makes the energy - saving strategy more intelligent, and the average energy - saving ratio is increased to 9.35%.

[0070] Reduce load fluctuations and improve equipment utilization: The optimized energy - consumption scheduling scheme reduces load instability, improves equipment utilization, and ensures the stability of energy supply.

[0071] Achieve closed - loop optimization and improve long - term energy - saving effects: Through the feedback mechanism and adaptive error analysis, the system can continuously improve the energy - saving strategy, making the energy - saving effect continuously improve during long - term operation.

[0072] This experiment fully verifies the effectiveness of the present invention in practical applications. Compared with traditional methods, it has higher energy - saving potential, more accurate energy - consumption prediction ability, and a more intelligent dynamic optimization mechanism, which can significantly improve the energy - management level of industrial parks or intelligent buildings.

[0073] Embodiment 3, which is an embodiment of the present invention, provides an energy - saving prediction management system, including a pre - processing module 100, a prediction module 200, and an optimization module 300.

[0074] Among them, S4: The pre - processing module 100 is used to collect multi - source data of energy - using terminals and perform pre - processing.

[0075] It should be noted that the preprocessing module 100 is used to collect multi-source data from energy-using terminals and preprocess the data to ensure the accuracy of subsequent energy consumption prediction. First, this module connects to the data sources in the park and collects various data. Subsequently, through the time synchronization mechanism, the data of different devices are adjusted to a unified time reference to ensure temporal consistency. After data collection, data cleaning is performed to remove abnormal data, duplicate data, and fill in missing data using linear interpolation to ensure data integrity. In addition, to reduce the data dimension and improve the calculation efficiency, mutual information analysis is carried out to screen out the features that have the most influence on energy consumption prediction and provide high-quality input data for the prediction module 200.

[0076] S5: The prediction module 200 is used to extract features and perform data analysis on the preprocessed data, and perform energy consumption prediction based on a deep neural network model.

[0077] It should be noted that the prediction module 200 receives the feature data provided by the preprocessing module 100 and conducts deep learning modeling to predict future energy consumption trends. First, this module performs time series segmentation, structuring the data according to different time windows to meet the energy consumption analysis requirements at different levels. Then, this module adopts a feature selection method based on multi-layer nested clustering to extract the global pattern and local pattern of the park's energy consumption data respectively, so as to enhance the sensitivity of the prediction model to energy consumption changes. Subsequently, based on the Laplace variational autoencoder (L-VAE), the input features are dimensionally reduced to reduce data redundancy and improve the calculation efficiency of the model. In terms of energy consumption prediction modeling, this module adopts a Transformer prediction network that combines a gated recurrent unit (GRU) and a local spatio-temporal attention mechanism (LSTA), where GRU is used to learn long-term energy consumption trends and LSTA is used to enhance the modeling ability for short-term load fluctuations. Finally, this module outputs the energy consumption prediction values at different future time steps and calculates the uncertainty interval of the prediction, so that the optimization module 300 can formulate precise energy-saving strategies.

[0078] S6: The optimization module 300 is used to generate energy-saving strategies based on the prediction results and perform dynamic optimization and adjustment through a feedback mechanism.

[0079] It should be noted that the optimization module 300 formulates an energy-saving strategy based on the energy consumption prediction results generated by the prediction module 200 and performs dynamic optimization and adjustment through a feedback mechanism. First, this module establishes a multi-objective optimization model, comprehensively considering factors such as energy-saving cost, load balancing, equipment life, and user comfort, to construct an optimal energy-saving scheduling plan. In terms of strategy evaluation, this module adopts an intelligent optimization method based on reinforcement learning (DQN+MCTS), where DQN is used to continuously optimize the strategy weights, and MCTS is used to screen the globally optimal strategy. The optimized energy-saving strategy includes measures such as adjusting the equipment operation time, intelligent load distribution, peak load reduction, and intelligent switch management to minimize the energy consumption expenditure. After the strategy is executed, this module forms a feedback data set for the execution of the energy-saving strategy by real-time monitoring the equipment operation data, environmental factors, and load levels. Subsequently, an adaptive error analysis model is used to calculate the deviation between the actual energy consumption value and the predicted value, and the reward function of the reinforcement learning model is adjusted in combination with Bayesian optimization, so that the optimized strategy can continuously adapt to the dynamic load changes in the park. Finally, this module redeploys the optimized energy-saving strategy to achieve the closed-loop optimization of the energy-saving strategy and ensure the long-term efficient operation of the energy management system.

[0080] If a function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0081] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch instructions from the instruction execution system, apparatus, or device and execute the instructions), or used in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

[0082] More specific examples (a non-exhaustive list) of computer-readable media include the following: electrical connections (electronic devices) having one or more wirings, portable computer diskettes (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber devices, and portable compact disc read-only memory (CDROM). Additionally, the computer-readable media can even be paper or other suitable media on which a program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing it in a suitable manner if necessary, and then storing it in a computer memory.

[0083] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having suitable combinational logic gate circuits, programmable gate arrays (PGA), field programmable gate arrays (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A management method for energy saving prediction, characterized in that: include: Collect multi-source data from energy usage terminals and pre-process them; Perform feature extraction and data analysis on the preprocessed data, and perform energy consumption prediction based on the deep neural network model; Generate energy-saving strategies based on prediction results and make dynamic optimization adjustments through feedback mechanisms; The deep neural network model includes input layer, variational autoencoder layer, time series modeling layer, Transformer prediction layer, fully connected layer and output layer; The input layer receives the feature set that affects the energy consumption prediction of the park and embeds it into the high-dimensional feature space through the multi-head self-attention mechanism; The variational autoencoder layer introduces the Laplace variational autoencoder to reduce the feature dimension of the input park energy consumption data, remove redundant information, optimize data representation, use the Laplace prior distribution to regularize the latent variables output by the encoder, optimize the variational lower bound through Laplace variational inference, and combine the decoder reconstruction error with KL divergence balance to generate low-dimensional interpretable feature representation; The time series modeling layer models the short-term changes in energy consumption data through gated recurrent units and dynamically adjusts the hidden state in combination with the attention mechanism; The Transformer prediction layer adopts a local spatiotemporal attention mechanism, adds local time window control to the global Transformer architecture, dynamically divides the input sequence based on the sliding time window, applies local attention weights to the time steps in the window, and adjusts the global and local attention contribution ratios through a time weighting mechanism, optimizing the joint modeling capabilities of short-term energy consumption fluctuations and long-term trends. The fully connected layer processes the temporal features output by the Transformer layer and uses the ReLU activation function to enhance the nonlinear mapping capability; The output layer converts the output of the fully connected layer into the final energy consumption prediction result.

2. The energy saving prediction management method according to claim 1, characterized in that: The pre-processing comprises: Collect the energy consumption data of the park through the energy usage terminal, and align the data based on the time synchronization mechanism; The park energy consumption data after time series alignment is cleaned, including removing duplicate data, abnormal data, and filling in missing data.

3. The energy saving prediction management method according to claim 1 or 2, characterized in that: The feature extraction and data analysis include: The preprocessed park energy consumption data is segmented into time series, and the feature selection method based on multi-layer nested clustering is used to classify and summarize the global and local patterns of the park energy consumption data. The mutual information analysis is combined to calculate the degree of mutual influence between the feature variables, and the feature set with the most significant impact on the park energy consumption prediction is screened.

4. The energy saving prediction management method according to claim 3, characterized in that: The deep neural network model includes: The input layer receives the feature set that has the most significant impact on the energy consumption prediction of the park, and uses a multi-head self-attention embedding mechanism to map the multi-dimensional input data into a high-dimensional feature space; The variational autoencoder layer introduces the Laplace variational autoencoder to reduce the feature dimension of the input park energy consumption data, remove redundant information, optimize data representation, and use the Laplace prior distribution to regularize the latent variables output by the encoder. The variational lower bound is optimized through Laplace variational inference, and the modeling ability of the sparsity and mutation characteristics of energy consumption data is optimized. The decoder reconstruction error and KL divergence balance are combined to generate a low-dimensional interpretable feature representation. The time series modeling layer uses gated recurrent units for time series modeling, learning short-term and long-term energy consumption trends from the input time series energy consumption data; dynamically adjusting the hidden state weights of the gated recurrent units through the attention mechanism; The Transformer prediction layer adopts a local spatiotemporal attention mechanism, adds local time window control to the global Transformer architecture, dynamically divides the input sequence based on the sliding time window, applies local attention weights to the time steps in the window, and adjusts the global and local attention contribution ratios through a time weighting mechanism, optimizing the ability to jointly model short-term energy consumption fluctuations and long-term trends. The fully connected layer receives the time series features output by the Transformer prediction layer and performs nonlinear transformation, using the ReLU activation function to enhance the nonlinear fitting capability; The output layer converts the output of the fully connected layer into the final energy consumption prediction result.

5. The energy saving prediction management method according to claim 1, 2 or 4, characterized in that: The energy consumption forecast includes: The feature set that has the most significant impact on the energy consumption prediction of the park is standardized and converted to the same scale using the Z-score normalization method; Use an embedding layer based on multi-head self-attention to input feature set data of the same scale into a deep neural network to capture the long-term and short-term dependencies between different features; Based on the variational autoencoder, feature dimensionality reduction is performed to convert the features into low-dimensional latent vector representations, and the gated recurrent unit GRU is introduced for time series modeling to extract the time series characteristics of energy consumption changes; The output result of GRU is input into the fully connected layer, and the Softmax activation function is used to calculate the probability distribution of park energy consumption at different time steps. Combined with the Bayesian regression model calculation, the final park energy consumption prediction result is obtained.

6. The energy saving prediction management method according to claim 5, characterized in that: The generating of energy-saving strategies according to the prediction results comprises: The energy saving strategy of the park is calculated and generated according to the energy consumption forecast results of the park. The reinforcement learning algorithm is used to evaluate the adaptability of different strategies and select the optimal energy-saving strategy.

7. The energy saving prediction management method according to claim 1, 2, 4 or 6, characterized in that: The dynamic optimization and adjustment through the feedback mechanism include: Execute the optimal energy-saving strategy, continuously monitor the actual energy consumption data, obtain the real-time equipment operation status, environmental parameters and load level, and form a feedback data set on the effect of energy-saving strategy execution; Based on the feedback data set, an adaptive error analysis model is used to calculate the error between the actual energy consumption value and the energy consumption forecast result of the park, and the error source is analyzed; According to the error analysis results, the energy-saving strategy is dynamically optimized.

8. A system using the energy saving prediction management method according to any one of claims 1 to 7, characterized in that: It includes a preprocessing module (100), a prediction module (200), and an optimization module (300); The preprocessing module (100) is used to collect multi-source data of energy use terminals and perform preprocessing; The prediction module (200) is used to perform feature extraction and data analysis on the preprocessed data, and to perform energy consumption prediction based on a deep neural network model; The optimization module (300) is used to generate an energy-saving strategy based on the prediction results, and to perform dynamic optimization and adjustment through a feedback mechanism.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the energy saving prediction management method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the energy saving prediction management method according to any one of claims 1 to 7 are implemented.

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