CN116736893B - Intelligent energy management method of optical storage device and optical storage device - Google Patents
Intelligent energy management method of optical storage device and optical storage device Download PDFInfo
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Abstract
An intelligent energy management method of an optical storage device and the optical storage device, which acquire solar position data and solar irradiation intensity data of a plurality of preset time points in a preset time period; and determining an angle adjustment strategy of a solar panel of the light storage device based on the solar position data and the solar irradiation intensity data at a plurality of predetermined time points within the predetermined time period. In this way, the solar panel can be made to receive maximum irradiation energy from the sun to increase productivity efficiency.
Description
Technical Field
The application relates to the technical field of intelligent management, in particular to an intelligent energy management method of an optical storage device and the optical storage device.
Background
A photovoltaic device is a device capable of converting solar energy into electrical energy and storing the electrical energy, and is generally composed of a solar photovoltaic panel and an energy storage system, and can convert solar energy into direct current electrical energy and store the direct current electrical energy in a battery or other energy storage devices for subsequent use.
In energy management of an optical storage apparatus, how to improve productivity efficiency of the optical storage apparatus is an important technical problem. The solar panels of existing light storage devices typically have a fixed angle, i.e. they cannot be adjusted angularly based on the sun position and the irradiation intensity for capacity optimization.
Therefore, an optimized light storage device and its intelligent energy management scheme are desired.
Disclosure of Invention
The present application has been made to solve the above-mentioned technical problems. The embodiment of the application provides an intelligent energy management method of a light storage device and the light storage device, which are used for acquiring solar position data and solar irradiation intensity data of a plurality of preset time points in a preset time period; and determining an angle adjustment strategy of a solar panel of the light storage device based on the solar position data and the solar irradiation intensity data at a plurality of predetermined time points within the predetermined time period. In this way, the solar panel can be made to receive maximum irradiation energy from the sun to increase productivity efficiency.
In a first aspect, an intelligent energy management method of an optical storage apparatus is provided, which includes:
acquiring solar position data and solar irradiation intensity data of a plurality of preset time points in a preset time period; and
an angular adjustment strategy for a solar panel of the light storage device is determined based on solar position data and solar irradiance intensity data for a plurality of predetermined time points over the predetermined time period.
In a second aspect, there is provided a light storage device operating in a smart energy management method of the light storage device as described.
Drawings
FIG. 1 is a flowchart of an intelligent energy management method of an optical storage apparatus according to an embodiment of the application.
Fig. 2 is a schematic diagram of an intelligent energy management method of an optical storage apparatus according to an embodiment of the application.
FIG. 3 is a flowchart illustrating the sub-steps of step 120 in the intelligent energy management method of the optical storage apparatus according to the embodiment of the present application.
Fig. 4 is a block diagram of an intelligent energy management system according to an embodiment of the present application.
Fig. 5 is a schematic view of a smart energy management method of an optical storage device according to an embodiment of the application.
Detailed Description
It should be understood that a light storage device is a device that uses solar energy for energy storage by converting the solar energy into electrical or other forms of energy and storing it for later use.
Light storage devices are generally composed of several major components:
1. the solar cell panel is a core component of the light storage device and can directly convert sunlight into electric energy. Solar cells in solar panels convert light energy into electrical energy using the photovoltaic effect. Solar panels are typically made of silicon materials, while solar panels of other materials, such as thin film solar panels and multi-junction solar panels, have also emerged in recent years.
2. An energy storage system for storing electrical energy generated from the solar panels for supply to the electrical devices when required. The energy storage system comprises a battery pack, a super capacitor, a heat storage system and the like. These systems are capable of converting electrical energy into chemical, potential or thermal energy and, if desired, into electrical energy again for supply to a user.
3. An inverter is a device that converts direct-current electric energy into alternating-current electric energy. Because most household appliances and industrial equipment use alternating current electric energy, the inverter plays an important role in the light storage device, and the direct current electric energy obtained from the solar cell panel and the energy storage system is converted into alternating current electric energy so as to meet the requirements of different equipment.
4. The control system is an intelligent management part of the light storage device and is used for optimally controlling the light storage device by monitoring information such as the output of the solar cell panel, the state of the energy storage system, the requirement of a user and the like. The control system can adjust the angle of the solar cell panel according to the real-time solar position and irradiation intensity data so as to maximize the energy collection efficiency.
The advantages of the light storage device include renewable energy utilization, no pollution, long service life and the like, can be widely applied to the fields of families, industries, agriculture and the like, and provides a sustainable and environment-friendly solution for energy supply. With the continuous progress of technology, the efficiency and reliability of the light storage device are also continuously improved, and important contribution is made to the development of renewable energy sources.
Further, improving the productivity of the optical storage apparatus is an important necessity in energy management. The energy efficiency of the light storage device directly influences the energy utilization efficiency obtained from solar energy, and solar energy resources can be utilized to the maximum extent by improving the energy utilization efficiency of the light storage device, so that more solar energy can be converted into available electric energy or other forms of energy. This helps to reduce reliance on conventional energy sources and improve energy sustainability.
The increased energy production efficiency of the light storage means that more energy is extracted from the solar panel per unit area or unit capacity. The method can reduce the production cost of energy sources, so that solar power generation is more economical, and along with the maturation and popularization of solar energy technology, the reduction of the energy source cost is important to promote the popularization and application of renewable energy sources.
The increased capacity efficiency of the optical storage device may provide a more stable and reliable energy supply. By maximizing the energy collection efficiency, the light storage device can better meet the energy requirements of users and reduce the uncertainty of energy supply. This is particularly important for critical facilities, remote areas and energy supplies in emergency situations.
The improvement of productivity efficiency of the optical storage device can reduce negative effects on the environment. By more effectively utilizing solar energy resources, the demand for fossil fuels is reduced, greenhouse gas emissions and air pollution can be reduced, and environmental damage is reduced. This helps to promote sustainable development and cope with challenges of climate change.
Improving the productivity of the light storage device is a key problem in energy management, and by optimizing the design, control and management of the light storage device, more efficient, reliable and sustainable energy utilization can be realized, and important contribution is made to the development and energy transformation of renewable energy.
The technical concept of the application is that the light storage device is provided with the solar cell panel with the adjustable angle, and the angle adjustment algorithm is configured to adaptively adjust the angle of the solar cell panel relative to the sun based on the sun position data and the sun irradiation intensity data, so that the solar cell panel can receive the maximum irradiation energy from the sun to improve the productivity efficiency.
The angle-adjustable solar panel is a solar panel with adjustable angles, and the angles of the angle-adjustable solar panel can be automatically adjusted according to the position and the irradiation intensity of the sun so as to maximize the absorption of solar energy and the energy generation efficiency. Fixed angle solar panels are typically mounted in fixed locations and angles, which can result in different solar energy utilization efficiencies at different times and seasons. The solar panel with the adjustable angle can be adjusted in real time according to the position and irradiation intensity of the sun by using an adjustable bracket or mechanism.
This adjustable capability allows the solar panel to remain at an angle normal or near normal to the sun at all times, thereby maximizing solar energy absorption. The solar panel may be angled to more directly receive solar radiation when the sun is at a lower angle. When the sun is at a higher angle, the angle of the solar cell panel can be adjusted to reduce reflection and scattering, so that the energy generation efficiency is improved.
The angularly adjustable solar panels are generally equipped with sensors and control systems for obtaining data of the position of the sun and of the intensity of the irradiation and automatically adjusting the angle of the panel according to these data. The intelligent energy management system can realize self-adaptive adjustment, improve the energy utilization efficiency, reduce the energy cost and improve the reliability of the system.
The solar cell panel with the adjustable angle is an advanced solar technology, and the angle is automatically adjusted according to the position and the irradiation intensity of the sun so as to maximize the absorption of solar energy and the energy generation efficiency, thereby improving the performance of a solar energy system and promoting more effective utilization of renewable energy.
In some specific examples of the application, the angle-adjustable solar panel may be implemented by: first, single axis tracking system: such systems use a shaft to cause the solar panel to follow the movement of the sun, the panel being mounted on a support which can rotate about a horizontal or vertical axis. Second, dual axis tracking system: the system uses two shafts to make the solar panel follow the movement of the sun, and besides the rotation of the horizontal shaft, the bracket can also be inclined and adjusted in the vertical direction, so that the solar panel can be kept vertical to the sunlight in the horizontal and vertical directions. Third, tilt adjustment system: the system realizes the angle adjustment by adjusting the inclination angle of the solar panel, wherein the bracket is provided with an adjustable connecting piece, so that the inclination angle of the panel can be changed.
FIG. 1 is a flowchart of an intelligent energy management method of an optical storage apparatus according to an embodiment of the application. Fig. 2 is a schematic diagram of an intelligent energy management method of an optical storage apparatus according to an embodiment of the application. As shown in fig. 1 and 2, the intelligent energy management method of the light storage device includes: 110, acquiring solar position data and solar irradiation intensity data of a plurality of preset time points in a preset time period; and 120, determining an angle adjustment strategy of the solar panel of the light storage device based on the solar position data and the solar irradiation intensity data at a plurality of predetermined time points within the predetermined time period.
Wherein in said step 110, accurate acquisition of sun position and irradiance data is ensured. Astronomical observation data, meteorological data or special solar monitoring equipment can be used for collection, and the accuracy of the data is critical to a subsequent angle adjustment strategy. And, an appropriate time interval is selected to collect the data. Shorter time intervals may provide more accurate data, but may also increase the complexity of data processing and computation; longer time intervals may result in loss or inaccuracy of the information.
By acquiring the solar position and irradiation intensity data, a more accurate decision can be made based on actual conditions instead of depending on static angle setting, thereby being beneficial to improving the energy collection efficiency and the productivity of the light storage device. The data of a plurality of preset time points in the preset time period can provide dynamic knowledge of solar energy resources, and according to the data of different time points, the angle of the light storage device can be adjusted to adapt to the change of the sun position and the irradiation intensity, so that the energy collection efficiency is maximized.
In the step 120, the acquired solar position and irradiance data is analyzed and processed to extract useful information. The optimal angular adjustment strategy may be determined using machine learning, optimization algorithms, or rule-based methods. Furthermore, the angular adjustment range of the solar panel is determined to ensure proper adjustment under different solar position and irradiance conditions. The physical limitations of the solar panel and the range of adjustable angles are considered.
The angle adjustment strategy of the solar panel is determined according to the solar position and the irradiation intensity data, so that the energy collection efficiency of the light storage device can be maximized, and the angle of the solar panel is adjusted to the optimal position, so that the solar panel can fully absorb solar energy at different time points and different solar angles. Based on the dynamic solar position and irradiation intensity data, the angle adjustment strategy can adapt to the change of solar energy resources in real time. This helps to increase the response speed and adaptability of the light storage device, enabling it to flexibly cope with different weather and lighting conditions.
In the above steps, by acquiring the solar position and irradiation intensity data in a predetermined period of time and determining the angle adjustment strategy of the light storage device based on the data, the energy collection efficiency of the light storage device can be improved, and the self-adaptive and intelligent energy management can be realized.
In another aspect, in the technical solution of the present application, the angle adjustment algorithm includes the following steps. Specifically, in the step 110, sun position data and sun irradiation intensity data at a plurality of predetermined time points within a predetermined period of time are acquired. In the present application, first, solar position data and solar irradiation intensity data at a plurality of predetermined time points within a predetermined period of time are acquired, the solar position data and the solar irradiation intensity data providing practical conditions regarding solar energy resources. These data reflect the position and irradiance of the sun at different points in time, which can help determine the energy harvesting potential of the light storage device at different points in time.
The solar position and the irradiation intensity may vary over time, and by acquiring data at a plurality of predetermined points in time, dynamic information about the solar resource may be acquired. According to the data, the angle of the solar panel of the light storage device can be adjusted to adapt to the change of the position of the sun and the irradiation intensity, and the collection efficiency of energy sources is maximized.
Based on the solar position data and the solar irradiation intensity data, an optimal solar panel angle adjustment strategy can be determined by using an optimization algorithm or other methods, and solar energy can be absorbed to the maximum extent by adjusting the solar panel angle to an optimal position, so that the energy collection efficiency is improved.
The angle of the solar panel is adjusted according to the actual solar resource condition, so that the utilization rate and the capacity of energy can be maximized, the energy output of the light storage device can be improved, the energy waste can be reduced, and the overall efficiency of the system can be improved.
Acquiring solar position data and solar irradiance data at a plurality of predetermined time points over a predetermined period of time is critical to ultimately determining an angular adjustment strategy for a solar panel of a light storage device. The data provides information about the actual conditions and dynamic changes of the solar resources, helps to optimize the energy collection efficiency, and improves the energy utilization rate and the energy productivity. By intelligently adjusting the angle of the solar panel, the self-adaption and intelligent energy management of the light storage device can be realized.
Specifically, in the step 120, an angle adjustment strategy of the solar panel of the light storage device is determined based on the solar position data and the solar irradiation intensity data at a plurality of predetermined time points within the predetermined time period. Fig. 3 is a flowchart illustrating the substeps of step 120 in the intelligent energy management method of the light storage device according to the embodiment of the present application, as shown in fig. 3, determining an angle adjustment strategy of a solar panel of the light storage device based on solar position data and solar irradiation intensity data at a plurality of predetermined time points within the predetermined time period, including: 121, arranging the solar position data and solar irradiation intensity data of the plurality of preset time points into a solar position data time sequence vector and a solar irradiation intensity time sequence input vector according to a time dimension respectively; 122 extracting a solar position timing feature vector from the solar position data timing vector; 123 extracting an irradiation intensity timing feature vector from the solar irradiation intensity timing input vector; 124, performing feature interaction on the solar position time sequence feature vector and the irradiation intensity time sequence feature vector to obtain an energy time sequence feature vector; and, determining 125 an angular adjustment strategy for a solar panel of the light storage device based on the energy timing feature vector.
In the application, firstly, collected solar position data and solar irradiation intensity data are organized into time sequence vectors according to time sequence, so that the time dimension can be used as input to provide a basis for subsequent feature extraction and analysis. Then, by analyzing and processing the solar position data time sequence vector, key features of the solar position are extracted, and the features can comprise information such as altitude angle, azimuth angle and the like of the sun, and reflect the position change of the sun at different time points. And then, analyzing and processing the solar irradiation intensity time sequence input vector, and extracting key characteristics of the irradiation intensity. These features may include information on the mean, peak, trend of change, etc. of the irradiation intensity reflecting the change in solar irradiation intensity at different time points. Then, the solar position time sequence feature vector and the irradiation intensity time sequence feature vector are subjected to feature interaction to obtain an energy time sequence feature vector, wherein the feature interaction can be realized through simple mathematical operations (such as multiplication, addition and the like) or a more complex feature engineering method. This allows for fusion of information of solar position and irradiance, providing more comprehensive and comprehensive energy characteristics. Finally, using the energy timing feature vector, a machine learning, optimization algorithm, or rule-based method may be applied to determine an angular adjustment strategy for the light storage device solar panel. The methods can predict the optimal solar panel angle setting according to the information in the energy time sequence feature vector so as to maximize the energy collection efficiency.
Further, through feature extraction and analysis, the data driving decision of the angle adjustment strategy is realized by utilizing the time sequence features of the solar position and irradiation intensity data, so that the accuracy and adaptability of the decision are improved, and the optical storage device can make optimal angle adjustment according to the real-time solar resource condition.
Through characteristic interaction, the information of the sun position and the irradiation intensity is fused together, so that more comprehensive and comprehensive energy characteristics are obtained. The method is beneficial to more accurately evaluating the energy collection potential and providing more abundant information for the determination of the angle adjustment strategy.
By determining an angle adjustment strategy based on the energy timing feature vector, the energy collection efficiency of the light storage device can be maximized. The angle of the solar panel is adjusted to adapt to the change of the position and irradiation intensity of the sun, so that the solar panel is beneficial to absorbing solar energy to the maximum extent, and the utilization rate and the productivity of energy sources are improved.
That is, based on solar position data and solar irradiation intensity data within a predetermined period of time, an angle adjustment strategy of a solar panel of the light storage device may be determined through feature extraction and analysis, and feature interaction, thereby achieving maximum collection efficiency of energy and optimizing energy utilization.
And arranging the solar position data and the solar irradiation intensity data of the plurality of preset time points into a solar position data time sequence vector and a solar irradiation intensity time sequence input vector according to a time dimension respectively. That is, the sensor is used to collect the time sequence of the solar position data and the solar irradiation intensity data, and the time sequence of the solar position data and the solar irradiation intensity data is subjected to data structure normalization to obtain the time sequence vector of the solar position data and the time sequence input vector of the solar irradiation intensity, so that the time sequence feature of the solar position and the time sequence feature of the solar irradiation intensity are conveniently extracted subsequently.
The arrangement according to the time dimension means that solar position data and solar irradiation intensity data of a plurality of preset time points are organized into time sequence vectors according to time sequence so as to facilitate subsequent feature extraction and analysis.
Specifically, it is assumed that there are N predetermined time points, each of which corresponds to one data of the sun position and the sun irradiation intensity. The data may be acquired by sensors or other measuring devices. The process of arrangement in the time dimension is as follows:
1. and (3) arranging N solar position data according to a time sequence to form a solar position data time sequence vector. The solar position data for each point in time may include altitude, azimuth, etc. information of the sun. The aligned timing vectors can be expressed as: [ position data 1, position data 2, ], position data N ].
2. The solar irradiation intensity data time sequence input vector is formed by arranging N solar irradiation intensity data according to time sequence. The solar irradiation intensity data at each time point may represent information of intensity, energy, and the like of solar irradiation. The aligned timing vectors can be expressed as: irradiation intensity data 1, irradiation intensity data 2,...
By arranging according to the time dimension, the solar position data and the solar irradiation intensity data can be in one-to-one correspondence with the time points to form a time sequence vector. Such an arrangement facilitates subsequent feature extraction and analysis, as well as determination of angular adjustment strategies. The methods such as feature extraction, machine learning, optimization algorithm and the like can be performed by utilizing the time sequence vectors, so that intelligent energy management and energy efficiency optimization of the light storage device are realized.
In one embodiment of the application, extracting a solar position timing feature vector from the solar position data timing vector comprises: and passing the solar position data time sequence vector through a position sequence encoder based on a fully connected layer to obtain the solar position time sequence feature vector.
In one embodiment of the application, extracting an irradiance intensity timing feature vector from the solar irradiance intensity timing input vector comprises: and the solar irradiation intensity time sequence input vector passes through an irradiation intensity time sequence feature extractor based on a one-dimensional convolution layer to obtain the irradiation intensity time sequence feature vector.
Further, a solar position timing feature vector is extracted from the solar position data timing vector, and an irradiance intensity timing feature vector is extracted from the solar irradiance intensity timing input vector. In one specific example of the present application, extracting a solar position timing feature vector from the solar position data timing vector comprises: and passing the solar position data time sequence vector through a position sequence encoder based on a fully connected layer to obtain the solar position time sequence feature vector. That is, the solar position data timing vector is fully-connected encoded by the fully-connected layer-based position sequence encoder to capture solar position full-time domain correlation features. In this particular example of the application, extracting an irradiance intensity timing feature vector from the solar irradiance intensity timing input vector comprises: and the solar irradiation intensity time sequence input vector passes through an irradiation intensity time sequence feature extractor based on a one-dimensional convolution layer to obtain the irradiation intensity time sequence feature vector. That is, the solar irradiation intensity time sequence input vector is one-dimensionally convolutionally encoded by the irradiation intensity time sequence feature extractor based on the one-dimensional convolution layer to capture local time sequence correlation features of solar irradiation intensity.
It should be appreciated that passing the solar position data timing vector through a fully connected layer based position sequence encoder to derive the solar position timing feature vector, the fully connected layer can learn complex relationships and patterns between solar position data and encode it as a feature vector with lower dimensions.
Through the encoding process of the fully connected layer, the solar position time sequence feature vector can capture key features and modes in solar position data. These features can reflect the trend of the position change of the sun at different time points, including altitude, azimuth and other information, and are very important for subsequent energy management and optimization decisions.
The encoding process of the full connection layer can convert the original solar position data time sequence vector into a feature vector with lower dimensionality, thereby being beneficial to reducing the complexity and the calculation burden of the data and improving the efficiency of subsequent processing.
Likewise, converting solar irradiance intensity timing input vectors into irradiance intensity timing feature vectors may be performed by a one-dimensional convolutional layer-based irradiance intensity timing feature extractor. The one-dimensional convolution layer can capture local modes and trends in the irradiation intensity data through convolution operation, and important features related to irradiation intensity change are extracted.
The one-dimensional convolution layer can effectively identify local patterns and trends in solar irradiance data, such as changes in sunrise and sunset, short term fluctuations, and the like. These modes can provide important information about the change in solar irradiance intensity, helping to optimize the energy management strategy of the light storage device.
Through the feature extraction process of the one-dimensional convolution layer, the irradiation intensity time sequence feature vector can capture key features in solar irradiation intensity data, such as average irradiation intensity, fluctuation degree, change trend and the like. These features are important to optimize the angular adjustment strategy of the solar panel.
The solar position data time sequence vector and the solar irradiation intensity time sequence input vector can be converted into a solar position time sequence feature vector and an irradiation intensity time sequence feature vector with lower dimensionality through a position sequence encoder based on a full-connection layer and an irradiation intensity time sequence feature extractor based on a one-dimensional convolution layer. These feature vectors can provide important information about solar position and irradiance levels, providing a beneficial feature representation for intelligent energy management and capacity efficiency optimization.
In one embodiment of the present application, performing feature interaction on the solar position timing feature vector and the irradiation intensity timing feature vector to obtain an energy timing feature vector, includes: performing feature interactions between the solar position timing feature vector and the irradiance intensity timing feature vector using an inter-feature attention layer to obtain an initial energy timing feature vector; calculating transferable sensing factors of the solar position timing feature vector and the irradiance intensity timing feature vector relative to the initial energy timing feature vector to obtain a first transferable sensing factor and a second transferable sensing factor; weighting the solar position time sequence feature vector and the irradiation intensity time sequence feature vector by taking the first transferable sensing factor and the second transferable sensing factor as weights so as to obtain a weighted solar position time sequence feature vector and a weighted irradiation intensity time sequence feature vector; the inter-feature attention layer is used to perform feature interactions between the weighted solar position temporal feature vector and the weighted irradiance intensity temporal feature vector to obtain the energy temporal feature vector.
And performing feature interaction on the solar position time sequence feature vector and the irradiation intensity time sequence feature vector to obtain an energy time sequence feature vector. It should be understood that in the application scenario of the present application, the energy is determined by the synergistic effect of the energy position and the irradiation intensity, so in the technical solution of the present application, the synergistic effect of the solar position time sequence feature vector and the irradiation intensity time sequence feature vector in the time dimension needs to be calculated.
In particular, in one specific example of the present application, the solar position timing feature vector and the irradiance intensity timing feature vector are feature interacted using an inter-feature attention layer to obtain the energy timing feature vector. The process can be formulated as:
for the solar position timing feature vector and the irradiance intensity timing feature vector,and->It is transformed into two feature spaces s and t to calculate the degree between them:
wherein (1)>、/>、/>Is a learned weight matrix corresponding to the 1 x 1 convolution in fig. 1, i is an index of the output location, j represents an index of all possible locations.
Those of ordinary skill in the art will appreciate that the goal of conventional attention mechanisms is to learn an attention weight matrix that is applied to individual neural nodes of the current layer, giving them greater weight for those important nodes and less weight for those secondary nodes. Because each neural node contains certain characteristic information, the neural network can select information which is more critical to the current task target from a plurality of characteristic information through the operation. The attention mechanism proposed by the application is different, and more attention is paid to the dependency relationship among the characteristic information, namely, the dependency cooperative information between the solar position time sequence characteristic vector and the irradiation intensity time sequence characteristic vector.
In particular, in the technical solution of the application, the solar position temporal feature vector and the irradiance temporal feature vector express a full-time-domain correlation feature of solar position data and a local temporal correlation feature of solar irradiance intensity data, respectively, and thus have different feature representations corresponding to global and local in the temporal dimension. In this way, when the energy timing feature vector is obtained by performing feature interaction between the solar position timing feature vector and the irradiation intensity timing feature vector using the inter-feature attention layer, the solar position timing feature vector and the irradiation intensity timing feature vector may have different domain transfer differences to the feature representation of the energy timing feature vector, and thus, it is desirable to perform feature cross-domain interaction based on such domain transfer differences, thereby improving the expression effect of the obtained energy timing feature vector.
Based on this, the applicant of the present application refers to the solar position timing feature vector, e.g. denoted asAnd the irradiation intensity timing feature vector, for example, denoted +.>And the initial energy timing feature vector, e.g. denoted +.>Calculating a quantized transferable sensing factor of its transferable characteristics: calculating transferable sensing factors of the solar position timing feature vector and the irradiance intensity timing feature vector relative to the initial energy timing feature vector in an optimization formula to obtain a first transferable sensing factor and a second transferable sensing factor; wherein, the optimization formula is:
wherein (1)>Representing the solar position timing feature vector, +.>Indicating the irradiation intensityDegree timing feature vector, ">Representing the initial energy timing feature vector, < >>Is the +.f. of one of the solar position timing feature vector, the irradiance timing feature vector, the initial energy timing feature vector>Characteristic value of individual position->Is a logarithmic function based on 2, and +.>Is a weighted superparameter,/->Representing said first transferable sensing factor, -/->Representing the second transferable sensing factor.
The quantized transferable sensing factors of the transferable features are used for respectively estimating the domain uncertainty from the feature space domain to the classification target domain through the uncertainty measurement under the domain transfer, and the domain uncertainty estimation can be used for identifying the feature representation transferred between domains, so that the feature space time sequence feature vector and the irradiation intensity time sequence feature vector are weighted by the factors as weights and then are fused based on feature interaction, whether feature mapping is effectively transferred between domains or not can be identified through the cross-domain alignment of the feature space domain to the classification target domain, so that the transferable property of the transferable features in the solar position time sequence feature vector and the irradiation intensity time sequence feature vector is quantitatively sensed, the feature interaction fusion of inter-domain self-adaption is realized, and the expression effect of the energy time sequence feature vector is improved.
Finally, an angle adjustment strategy of the solar panel of the light storage device is determined based on the energy time sequence feature vector. Specifically, in the technical scheme of the application, the energy time sequence feature vector is passed through a classifier to obtain a classification result, and the angle value of the solar panel of the light storage device used for representing the current time point is required to be increased, reduced or kept unchanged. Further, an angle adjustment strategy of the solar panel of the light storage device is generated based on the classification result.
In one embodiment of the application, determining an angular adjustment strategy for a solar panel of the light storage device based on the energy timing feature vector comprises: the energy time sequence feature vector is passed through a classifier to obtain a classification result, wherein the classification result is used for indicating that the angle value of the solar panel of the light storage device at the current time point is increased, decreased or kept unchanged; and generating an angle adjustment strategy of the solar panel of the light storage device based on the classification result.
The classifier classifies the energy time sequence feature vectors, so that intelligent energy management of the light storage device can be realized. According to different energy conditions, the system can automatically adjust the angle of the solar panel so as to maximize the utilization efficiency of energy. For example, when the illumination is strong, the system can adjust the angle of the solar panel to be opposite to the sun so as to obtain more solar energy; and when the illumination is weaker, the system can adjust the angle of the solar panel to avoid energy waste.
Through the classification result of the classifier, the angle of the solar panel can be reasonably adjusted according to the current energy condition, so that the capacity and efficiency of the optical storage device are optimized. The solar energy can be captured to the greatest extent by dynamically adjusting the angle of the solar panel and converted into usable energy, so that the productivity efficiency of the light storage device is improved.
The energy time sequence feature vectors are classified by the classifier, so that the angle of the solar panel can be reasonably adjusted according to energy conditions, and the energy cost is reduced. By optimizing the utilization efficiency of solar energy, the dependence on traditional energy sources can be reduced, the energy source purchasing cost is reduced, and the negative influence on the environment is reduced.
Through the classification result of the classifier, the angle of the solar panel can be reasonably adjusted according to the energy condition, so that the system reliability of the light storage device is improved. Through the angle of dynamic adjustment solar panel, can avoid the emergence of energy waste and overload condition, the steady operation of protection light storage device.
In other words, the energy time sequence feature vectors are classified by the classifier, so that the adjustment strategy of the solar panel angle of the light storage device can be predicted according to the energy condition, and intelligent energy management and energy efficiency optimization are realized. This will lead to an intelligent, efficient and reliable energy management scheme, improving energy utilization efficiency, reducing energy costs, and reducing environmental impact.
In summary, the intelligent energy management method 100 of the light storage device according to the embodiment of the present application is illustrated, wherein an angle-adjustable solar panel is configured for the light storage device, and an angle adjustment algorithm is configured to adaptively adjust the angle of the solar panel relative to the sun based on the sun position data and the sun irradiation intensity data, so that the solar panel can receive the maximum irradiation energy from the sun to improve the productivity efficiency.
In one embodiment of the application, a light storage device is provided that operates with the intelligent energy management method of the light storage device as described.
In one embodiment of the present application, FIG. 4 is a block diagram of an intelligent energy management system according to an embodiment of the present application. As shown in fig. 4, the intelligent energy management system 200 according to an embodiment of the present application includes: a data acquisition module 210 for acquiring solar position data and solar irradiation intensity data at a plurality of predetermined time points within a predetermined period of time; and an angle adjustment strategy determination module 220 for determining an angle adjustment strategy of the solar panel of the light storage device based on the solar position data and the solar irradiation intensity data at a plurality of predetermined time points within the predetermined time period.
Here, it will be understood by those skilled in the art that the specific functions and operations of the respective units and modules in the above-described smart energy management system have been described in detail in the above description of the smart energy management method of the light storage device with reference to fig. 1 to 3, and thus, repetitive descriptions thereof will be omitted.
As described above, the intelligent energy management system 200 according to the embodiment of the present application may be implemented in various terminal devices, such as a server for intelligent energy management, etc. In one example, the intelligent energy management system 200 according to an embodiment of the present application may be integrated into the terminal device as a software module and/or hardware module. For example, the intelligent energy management system 200 may be a software module in the operating system of the terminal device, or may be an application developed for the terminal device; of course, the intelligent energy management system 200 can also be one of a plurality of hardware modules of the terminal device.
Alternatively, in another example, the intelligent energy management system 200 and the terminal device may be separate devices, and the intelligent energy management system 200 may be connected to the terminal device through a wired and/or wireless network and transmit interactive information in a contracted data format.
Fig. 5 is a schematic view of a smart energy management method of an optical storage device according to an embodiment of the application. As shown in fig. 5, in the application scenario, first, sun position data (e.g., C1 as illustrated in fig. 5) and sun irradiation intensity data (e.g., C2 as illustrated in fig. 5) at a plurality of predetermined time points within a predetermined period of time are acquired; the acquired solar position data and solar irradiance intensity data are then input into a server (e.g., S as illustrated in fig. 5) deployed with a smart energy management algorithm, wherein the server is capable of processing the solar position data and the solar irradiance intensity data based on the smart energy management algorithm to determine an angular adjustment strategy for a solar panel of the light storage device.
It is also noted that in the apparatus, devices and methods of the present application, the components or steps may be disassembled and/or assembled. Such decomposition and/or recombination should be considered as equivalent aspects of the present application.
The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the application. Thus, the present application is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The foregoing description has been presented for purposes of illustration and description. Furthermore, this description is not intended to limit embodiments of the application to the form disclosed herein. Although a number of example aspects and embodiments have been discussed above, a person of ordinary skill in the art will recognize certain variations, modifications, alterations, additions, and subcombinations thereof.
Claims (6)
1. An intelligent energy management method for an optical storage device is characterized by comprising the following steps:
acquiring solar position data and solar irradiation intensity data of a plurality of preset time points in a preset time period; and
determining an angle adjustment strategy of a solar panel of the light storage device based on solar position data and solar irradiation intensity data at a plurality of predetermined time points within the predetermined time period;
determining an angular adjustment strategy for a solar panel of a light storage device based on solar position data and solar irradiance intensity data for a plurality of predetermined time points over the predetermined time period, comprising:
arranging the solar position data and the solar irradiation intensity data of the plurality of preset time points into a solar position data time sequence vector and a solar irradiation intensity time sequence input vector according to a time dimension respectively;
extracting a solar position timing feature vector from the solar position data timing vector;
extracting an irradiation intensity time sequence feature vector from the solar irradiation intensity time sequence input vector;
performing feature interaction on the solar position time sequence feature vector and the irradiation intensity time sequence feature vector to obtain an energy time sequence feature vector; and
determining an angle adjustment strategy of a solar panel of the light storage device based on the energy time sequence feature vector;
the performing feature interaction on the solar position time sequence feature vector and the irradiation intensity time sequence feature vector to obtain an energy time sequence feature vector comprises the following steps:
performing feature interactions between the solar position timing feature vector and the irradiance intensity timing feature vector using an inter-feature attention layer to obtain an initial energy timing feature vector;
calculating transferable sensing factors of the solar position timing feature vector and the irradiance intensity timing feature vector relative to the initial energy timing feature vector to obtain a first transferable sensing factor and a second transferable sensing factor;
weighting the solar position time sequence feature vector and the irradiation intensity time sequence feature vector by taking the first transferable sensing factor and the second transferable sensing factor as weights so as to obtain a weighted solar position time sequence feature vector and a weighted irradiation intensity time sequence feature vector;
the inter-feature attention layer is used to perform feature interactions between the weighted solar position temporal feature vector and the weighted irradiance intensity temporal feature vector to obtain the energy temporal feature vector.
2. The intelligent energy management method of a light storage apparatus of claim 1, wherein extracting a solar position timing feature vector from the solar position data timing vector comprises: and passing the solar position data time sequence vector through a position sequence encoder based on a fully connected layer to obtain the solar position time sequence feature vector.
3. The intelligent energy management method of a light storage device according to claim 2, wherein extracting an irradiance intensity timing feature vector from the solar irradiance intensity timing input vector comprises: and the solar irradiation intensity time sequence input vector passes through an irradiation intensity time sequence feature extractor based on a one-dimensional convolution layer to obtain the irradiation intensity time sequence feature vector.
4. A method of intelligent energy management of a light storage apparatus according to claim 3, wherein calculating transferable sensing factors of the solar position timing feature vector and the irradiance intensity timing feature vector relative to the initial energy timing feature vector to obtain a first transferable sensing factor and a second transferable sensing factor comprises: calculating transferable sensing factors of the solar position timing feature vector and the irradiance intensity timing feature vector relative to the initial energy timing feature vector in an optimization formula to obtain a first transferable sensing factor and a second transferable sensing factor;
wherein, the optimization formula is:wherein (1)>Representing the solar position timing feature vector, +.>Representing the irradiation intensity time sequence feature vector, +.>Representing the initial energy timing feature vector, < >>Is the +.f. of one of the solar position timing feature vector, the irradiance timing feature vector, the initial energy timing feature vector>Characteristic value of individual position->Is a logarithmic function based on 2, and +.>Is a weighted superparameter,/->Representing said first transferable sensing factor, -/->Representing the second transferable sensing factor.
5. The intelligent energy management method of a light storage apparatus according to claim 4, wherein determining an angle adjustment strategy of a solar panel of the light storage apparatus based on the energy timing feature vector comprises:
the energy time sequence feature vector is passed through a classifier to obtain a classification result, wherein the classification result is used for indicating that the angle value of the solar panel of the light storage device at the current time point is increased, decreased or kept unchanged; and
and generating an angle adjustment strategy of the solar panel of the light storage device based on the classification result.
6. A light storage device, characterized in that the light storage device operates with the intelligent energy management method of the light storage device according to any one of claims 1 to 5.
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