CN111800209B - Solar energy prediction method based on energy model and dynamic weight factor - Google Patents
Solar energy prediction method based on energy model and dynamic weight factor Download PDFInfo
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Abstract
The invention relates to a solar energy prediction method based on an energy model and a dynamic weight factor, and belongs to the field of wireless communication energy collection. And searching the most similar energy model from the historical energy models, and based on the most similar energy model, performing energy prediction of the next time slot. And obtaining the most similar historical energy model according to the minimum average error of the energy values of the K time slots before the nth +1 time slot of the day and the energy difference value of the corresponding time slot in the previous D days. In order to dynamically reflect the influence of weather change on the prediction result, the invention sets the dynamic weight factor, so that the weight factor can be correspondingly adjusted along with the weather change, and the contribution degree of each component in the prediction model to the prediction result is better reflected, thereby improving the prediction precision of solar energy collection. The method is simple, efficient, low in complexity, easy to implement on the actual wireless sensor node and good in practicability.
Description
Technical Field
The invention belongs to the field of wireless communication energy collection, and relates to a solar energy prediction method based on an energy model and a dynamic weight factor.
Background
The wireless sensor network is widely applied to the fields of industrial and agricultural production, medical service, environmental monitoring, home security, military and the like. The continuous energy supply of the sensor nodes is a big bottleneck restricting the practical application of the wireless sensor network. The solar energy is collected to supply energy to the sensor nodes, and the method is an effective way for solving the problem of continuous energy supply of the wireless sensor network. Since solar energy collection is greatly affected by factors such as daytime, weather, region, and the like, and a continuous and stable power supply cannot be provided, it is necessary to predict and manage the solar energy collection in order to reasonably utilize the solar energy, so as to effectively utilize the solar energy. Solar energy prediction is a precondition for solar energy management and distribution, and accurately and effectively predicts solar energy collected in a short term and a long term, so that a foundation is provided for subsequent energy distribution and management, and the utilization efficiency of the solar energy is effectively improved. The currently common solar Energy prediction methods mainly comprise EWMA, WCMA, Pro-Energy, UD-WCMA and the like.
Most of the above methods use the energy value collected in the previous time slot as a basic component of energy prediction in the next time slot, but under the condition of large weather fluctuation, the data in the previous time slot is far from providing a powerful reference for the next time slot, which leads to a sharp increase of prediction error and a great reduction of algorithm accuracy. Meanwhile, the weight factors in most prediction methods are fixed values, and along with the continuous change of the weather state, the fixed weight factors cannot follow the change of the weather, so that the contribution degree of each component in the prediction model to the prediction result cannot be well reflected, and the prediction precision is reduced.
Disclosure of Invention
In view of the above, the present invention provides a solar energy prediction method based on an energy model and a dynamic weighting factor. According to the method, the most similar historical energy collection model is selected as the basis of the prediction model, the contribution degrees of the two parts are measured in real time by combining with the weather change factor and adopting the dynamic weight factor, and the accuracy of energy prediction when the weather changes violently is improved.
In order to achieve the purpose, the invention provides the following technical scheme:
a solar energy prediction method based on an energy model and a dynamic weight factor comprises the following steps:
s1: dividing a natural day into N time slots, recording the solar energy value collected by each time slot, and storing N energy sample values obtained in the natural day as a weather model; recording the solar energy collected in the past D days to obtain D historical energy models;
s2: marking the current day as D day, and selecting a most similar historical energy model E (i) from the past D days according to the solar energy values collected from the first K time slots of the (n +1) th time slot of the current day*)；
S3: calculating the weather condition factor GAP of the (n +1) th time slotn+1And a dynamic weight factor alphan+1；
S4: according to the most similar historical energy model, the weather condition factor and the dynamic weight factor, the following solar energy prediction method is obtained:
wherein,
for the predicted energy value of the (n +1) th slot of the day, E (i)*N +1) is the energy value of the (n +1) th time slot in the most similar energy model, GAPn+1Weather condition factor for the (n +1) th time slot, MD(D, n +1) is the average energy value of the n +1 time slot on the previous D days.
Optionally, in S2, the specific step of selecting the most similar historical energy model is as follows:
calculating the average error of the energy collected by the first K time slots of the (n +1) th time slot on the day and the K corresponding time slots on the ith day in the past D days, and expressing the average error as follows:
where E (d, k) and E (i, k) represent the energy collected at the k-th slot on the current day and the i-th day, respectively, pk-n+KIs the corresponding weight; according to the past K time slots toTime interval of current time slot is assigned weight, pk-n+KA weight vector P is composed, represented as:
the energy model with the minimum average error is the most similar energy model, and the corresponding daily times are represented by i; the selection method of the most similar historical energy model of the (n +1) th time slot in the current day is represented as follows:
optionally, in S3, the specific steps of the weather condition factor GAP are as follows:
the average energy values collected at the n +1 th time slot on the past D days are:
defining a vector having K elements, V ═ V1,v2,…,vk-n+K,…,vK]Element v thereofk-n+KThe ratio of the energy value of the first K time slots of the (n +1) th time slot of the day to the average energy of the corresponding time slots of the last D days is expressed as:
then the weather condition factor for the (n +1) th slot of the day is expressed as:
optionally, in S3, a dynamic weighting factor α is calculatedn+1The method comprises the following specific steps:
computing weather condition factor and energy for the nth time slotProduct of mean values GAPn×MDAbsolute value lambda of the difference between (d, n) and the energy value E (d, n) acquired in the nth time slot1：
λ1＝|GAPn×MD(d,n)-E(d,n)| (8)
Calculating the absolute value lambda of the difference between the energy value of the nth time slot in the most similar weather model and the energy value of the nth time slot in the current day2：
The dynamic weight factor for the (n +1) th slot is expressed as:
the invention has the beneficial effects that:
(1) aiming at the problem that the solar energy value collected in the previous time slot cannot provide powerful support for the energy prediction of the next time slot under the condition of large weather fluctuation, the most similar energy model is searched from the historical energy model, and on the basis of the most similar energy model, the energy prediction of the next time slot is carried out, so that the prediction error is reduced, and the algorithm accuracy is improved.
(2) According to the invention, in the aspect of weight setting, the weight factor is dynamically calculated in each time slot, so that the weight factor can be correspondingly adjusted along with weather change, and the contribution degree of each component in the prediction model to the prediction result is well reflected, thereby improving the prediction precision.
(3) According to the method, the historical energy model is fully utilized, and the weight factor is dynamically adjusted, so that the prediction model can follow the change of weather as much as possible, and the accuracy of solar energy collection prediction is effectively improved; the method is simple to implement, low in complexity, easy to implement on an actual wireless sensor node and good in practicability.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
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For the purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made to the following detailed description taken in conjunction with the accompanying drawings in which:
FIG. 1 is a diagram of slot assignments for each day;
FIG. 2 is a flow chart of the present invention.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention in a schematic way, and the features in the following embodiments and examples may be combined with each other without conflict.
Wherein the showings are for the purpose of illustrating the invention only and not for the purpose of limiting the same, and in which there is shown by way of illustration only and not in the drawings in which there is no intention to limit the invention thereto; to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it should be understood that if there is an orientation or positional relationship indicated by terms such as "upper", "lower", "left", "right", "front", "rear", etc., based on the orientation or positional relationship shown in the drawings, it is only for convenience of description and simplification of description, but it is not an indication or suggestion that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes, and are not to be construed as limiting the present invention, and the specific meaning of the terms may be understood by those skilled in the art according to specific situations.
As shown in fig. 1, a slot allocation diagram of the present invention is shown. Each day is divided into N time slots, labeled 1, 2, …, N, each time slot recording the amount of energy collected by that time slot. Storing N energy sample values obtained in one day as a weather model; and recording the solar energy collected in the past D days to obtain D historical energy models.
Fig. 2 is a flowchart of a solar energy prediction method based on an energy model and dynamic weighting factors according to the present invention. Dividing a day into N time slots, recording the solar energy value collected by each time slot, and storing N energy sample values obtained in the day as a weather model; and recording the solar energy collected in the past D days to obtain D historical energy models. And the current day is marked as the D-th day (D is more than or equal to D), and a most similar historical energy model is selected from the past D days according to the solar energy values collected from the first K time slots (K is less than or equal to n) of the n +1 time slot of the current day to calculate the weather condition factor and the dynamic weight factor of the n +1 time slot. And obtaining the predicted energy value of the solar energy in the next time slot according to the most similar historical energy model, the weather condition factor and the dynamic weight factor.
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.
Claims (3)
1. A solar energy prediction method based on an energy model and a dynamic weight factor is characterized in that: the method comprises the following steps:
s1: dividing a natural day into N time slots, recording the solar energy value collected by each time slot, and storing N energy sample values obtained in the natural day as a weather model; recording the solar energy collected in the past D days to obtain D historical energy models;
s2: marking the current day as D day, and selecting a most similar historical energy model E (i) from the past D days according to the solar energy values collected from the first K time slots of the (n +1) th time slot of the current day*)；
S3: calculating the weather condition factor GAP of the (n +1) th time slotn+1And a dynamic weight factor alphan+1；
S4: according to the most similar historical energy model, the weather condition factor and the dynamic weight factor, the following solar energy prediction method is obtained:
wherein,
for the predicted energy value of the (n +1) th slot of the day, E (i)*N +1) is the energy value of the (n +1) th time slot in the most similar energy model, GAPn+1Weather condition factor for the (n +1) th time slot, MD(D, n +1) is the average energy value of the n +1 time slot of the previous D days;
in S3, a dynamic weighting factor α is calculatedn+1The method comprises the following specific steps:
calculating the product GAP of the weather condition factor and the energy mean value of the nth time slotn×MDAbsolute value lambda of the difference between (d, n) and the energy value E (d, n) acquired in the nth time slot1：
λ1＝|GAPn×MD(d,n)-E(d,n)| (8)
Calculating the absolute difference between the energy value of the nth time slot in the most similar weather model and the energy value of the nth time slot in the current dayFor the value lambda2：
The dynamic weight factor for the (n +1) th slot is expressed as:
2. the solar energy prediction method based on the energy model and the dynamic weight factor as claimed in claim 1, wherein: in S2, the specific steps of selecting the most similar historical energy model are as follows:
calculating the average error of the energy collected by the first K time slots of the (n +1) th time slot on the day and the K corresponding time slots on the ith day in the past D days, and expressing the average error as follows:
where E (d, k) and E (i, k) represent the energy collected at the k-th slot on the current day and the i-th day, respectively, pk-n+KIs the corresponding weight; assigning weights, p, according to the time interval from the past K time slots to the current time slotk-n+KA weight vector P is composed, represented as:
the energy model with the minimum average error is the most similar energy model, and the corresponding daily times are represented by i; the selection method of the most similar historical energy model of the (n +1) th time slot in the current day is represented as follows:
3. the solar energy prediction method based on the energy model and the dynamic weight factor as claimed in claim 1, wherein: in S3, the specific steps of the weather condition factor GAP are as follows:
the average energy values collected at the n +1 th time slot on the past D days are:
defining a vector having K elements, V ═ V1,v2,…,vk-n+K,…,vK]Element v thereofk-n+KThe ratio of the energy value of the first K time slots of the (n +1) th time slot of the day to the average energy of the corresponding time slots of the last D days is expressed as:
then the weather condition factor for the (n +1) th slot of the day is expressed as:
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