Method for evaluating the loadability of a multi-functional washing machine

Abstract

The application discloses a multifunctional washing machine load day-ahead adjustable capacity evaluation method, which comprises the following steps: collecting multifunctional washing machine load operation information and weather information; constructing a multifunctional washing machine behavior characteristic prediction model considering weather factors, and predicting the behavior characteristics of the multifunctional washing machine; constructing a multifunctional washing machine load correction model considering weather factors, and correcting the behavior of the multifunctional washing machine under different weather factors and electricity price information according to the behavior characteristics of the multifunctional washing machine to obtain the corrected load of the multifunctional washing machine; and constructing a multifunctional washing machine load day-ahead adjustable capacity evaluation model considering weather factors based on the corrected load of the multifunctional washing machine, and evaluating the multifunctional washing machine load day-ahead adjustable capacity. The application realizes fine modeling of the multifunctional washing machine, improves the multifunctional washing machine load prediction accuracy, and effectively evaluates the multifunctional washing machine load day-ahead adjustable capacity.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system regulation, specifically a multifunctional washing machine load day-ahead adjustable capacity evaluation method. BACKGROUND

[0002] By evaluating and utilizing the day-ahead adjustable capacity of multifunctional washing machine load, the power grid can better optimize energy distribution, improve the utilization efficiency of renewable energy, enhance demand response capability, and reduce operating costs. This is of great significance for coping with the complexity of future power demand, promoting the popularization of green energy, and promoting the development of smart grids. The existing methods indeed have the following shortcomings: 1) Lack of fine modeling, multifunctional washing machines with drying function involve multiple working modes such as washing, rinsing, dewatering, drying, etc., and the energy consumption and load characteristics of each mode are different. Existing methods often fail to model these functions in detail, usually using a single model to predict the entire process, which may not accurately capture the load characteristics of each stage, resulting in insufficient accuracy of overall load prediction and ineffective utilization of the schedulability of the equipment. 2) External environmental factors are not considered, weather, temperature, humidity, and electricity price have a significant impact on washing machine usage behavior. For example, when the humidity is high, users may prefer to use the drying function; during the low electricity price period, users may choose to delay washing. Existing models usually ignore these key factors, resulting in inaccurate prediction of user behavior, which affects the accuracy and reliability of washing machine load prediction. 3) Lack of evaluation method for adjustable capacity, the adjustable capacity of multifunctional washing machines, i.e. their flexibility in operation at different time periods and their response capability to grid demand, has not been fully evaluated. Existing methods often lack targeted evaluation tools or indicators to quantify this capacity, making it difficult for grid operators to develop optimized scheduling strategies and failing to fully utilize the potential flexibility of multifunctional washing machines to support load balancing and renewable energy consumption of the grid. SUMMARY

[0003] To solve the above problems, the present application proposes a multifunctional washing machine load day-ahead adjustable capacity evaluation method capable of improving the accuracy of multifunctional washing machine load prediction.

[0004] To achieve the above purpose, the present application is realized by the following technical scheme:

[0005] The present application is a multifunctional washing machine load day-ahead adjustable capacity evaluation method, comprising the following operations:

[0006] Collect multifunctional washing load operation information and weather information;

[0007] Construct a multifunctional washing machine behavior characteristic prediction model considering weather factors, and predict the behavior characteristics of the multifunctional washing machine;

[0008] A multifunctional washing machine load correction model considering weather factors is constructed, and the behavior of the multifunctional washing machine under different weather factors and electricity price information is corrected according to the behavior characteristics of the multifunctional washing machine, so as to obtain the corrected load of the multifunctional washing machine;

[0009] Based on the corrected load of the multifunctional washing machine, a multifunctional washing machine load day-ahead adjustable capacity evaluation model considering weather factors is constructed, and the multifunctional washing machine load day-ahead adjustable capacity is evaluated.

[0010] Further improvement of the present application is that the collected multifunctional washing load operation information includes washing machine operation time interval, washing machine start time, washing machine shutdown time, washing machine power, drying operation time power, and the collected weather information includes weather state, outdoor temperature, humidity, and the expression is:

[0011]

[0012] Wherein, X i (k) is the i-th day multifunctional washing machine load i basic data, is the i-th day multifunctional washing machine i start time, is the i-th day multifunctional washing machine i shutdown time, is the i-th day multifunctional washing machine drying load end time, is the i-th day multifunctional washing machine i operation time range, wherein is the i-th day multifunctional washing machine i earliest start time, is the i-th day multifunctional washing machine i latest start time, P i x is the washing power of multifunctional washing machine i, P i h is the drying power of multifunctional washing machine i, T(k) is the temperature of the k-th day, R(k) is the humidity of the k-th day, W(k) is the weather state of the k-th day, and the value is 1, indicating sunny day, the value is 2, indicating cloudy day, the value is 3, indicating rainy day, the value is 4, indicating snowy day, and ψ(k) is the electricity price information of the k-th day.

[0013] Further improvement of the present application is that a multifunctional washing machine behavior characteristic prediction model considering weather factors is constructed, specifically including: based on BP neural network, the collected multifunctional washing load operation information and weather information are trained, and a multifunctional washing machine behavior characteristic prediction model is constructed, wherein the BP neural network includes 4 inputs and 2 outputs, and the 4 inputs are: the temperature T(k) of the k-th day, the humidity R(k) of the k-th day, the weather state W(k) of the k-th day, and the electricity price information ψ(k) of the k-th day, and the 2 outputs are: δ x =1 indicates that the predicted multifunctional washing machine only starts the washing function, and δh = 1 indicates that the predicted multifunctional washing machine starts the washing function and the drying function, the hidden layer of the BP neural network adopts the tansig function, and the expression of the multifunctional washing machine behavior characteristic prediction model is:

[0014]

[0015] Wherein, f x () is the washing behavior prediction model of the BP neural network after training, f h () is the washing and drying behavior prediction model of the BP neural network after training.

[0016] Further improvement of the present application is that the multifunctional washing machine load correction model considering weather factors includes two cases of not using a dryer and using a dryer;

[0017] Wherein, the expression of the multifunctional washing machine load correction model under the condition of not using a dryer is:

[0018]

[0019] The expression of the multifunctional washing machine load correction model under the condition of using a dryer is:

[0020]

[0021] Wherein, δ x is the washing behavior predicted based on weather factors and electricity price information, δ x = 1 indicates that the predicted multifunctional washing machine only starts the washing function, P(t) is the power of the multifunctional washing machine i, δ h is the washing and drying behavior predicted based on weather factors and electricity price information, δ h = 1 indicates that the predicted multifunctional washing machine starts the washing function and the drying function, P i x (t) is the washing power of the multifunctional washing machine i at t time, P i h (t) is the drying power of the multifunctional washing machine i at t time.

[0022] Further improvement of the present application is that the expression of the multifunctional washing machine load day-ahead adjustable capacity evaluation model is:

[0023]

[0024]

[0025] Wherein, t x is the washing time, t h is the drying time, ΔP up(t) is up-regulated load, ΔP dn (t) is down-regulated load, M is the number of washing machines participating in up-regulation, N is the number of washing machines participating in down-regulation, is the start-up time of multifunctional washing machine i1 participating in up-regulation, is the shutdown time of multifunctional washing machine i1 participating in up-regulation, is the end time of drying load of multifunctional washing machine i1 participating in up-regulation, is the start-up time of multifunctional washing machine i2 participating in down-regulation, is the shutdown time of multifunctional washing machine i2 participating in down-regulation, is the end time of drying load of multifunctional washing machine i2 participating in down-regulation, is the washing power of multifunctional washing machine i1 at time t, is the drying power of multifunctional washing machine i1 at time t, is the washing power of multifunctional washing machine i2 at time t, is the drying power of multifunctional washing machine i2 at time t, wherein multifunctional washing machines i1 and i2 belong to i.

[0026] The beneficial effects of the present application are: the present application models the multifunctional washing machine in detail, improves the load prediction accuracy of the multifunctional washing machine, and realizes effective evaluation of the day-ahead adjustable capacity of the multifunctional washing machine load. BRIEF DESCRIPTION OF DRAWINGS

[0027] Figure 1 is a method flowchart of an embodiment of the present application;

[0028] Figure 2 is a multifunctional washing machine behavior characteristic prediction model schematic diagram in an embodiment of the present application;

[0029] Figure 3 is an uncorrected load schematic diagram in an embodiment of the present application;

[0030] Figure 4 is a corrected load schematic diagram in an embodiment of the present application. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application. In addition, the technical features involved in each embodiment of the present application described below can be combined with each other as long as they do not conflict with each other.

[0032] As shown in Figure 1 , the present embodiment is a multifunctional washing machine load day-ahead adjustable capacity evaluation method, which comprises the following operations:

[0033] Step 1, collect multifunctional washing load running information, weather information and electricity price information. The multifunctional washing machine in this embodiment has washing function and drying clothes function, and the multifunctional washing load running information includes washing machine running time interval, washing machine start and stop time, washing machine power, drying running power and the like. The weather information includes weather state, outdoor temperature, humidity and the like, wherein the weather state is, for example, sunny day, cloudy day, rainy day, snowy day and the like. The collected information is expressed by the following expression:

[0034]

[0035] wherein, X i (k) is the kth day multifunctional washing machine load i basic data, is the kth day multifunctional washing machine i start time, is the kth day multifunctional washing machine i stop time, is the kth day multifunctional washing machine drying load end time, is the kth day multifunctional washing machine i running time range, wherein is the kth day multifunctional washing machine i earliest start time, is the kth day multifunctional washing machine i latest start time, P i x is the washing power of multifunctional washing machine i, P i h is the drying power of multifunctional washing machine i, T(k) is the temperature of the kth day, R(k) is the humidity of the kth day, W(k) is the weather state of the kth day, taking value 1 represents sunny day, taking value 2 represents cloudy day, taking value 3 represents rainy day, taking value 4 represents snowy day, and ψ(k) is the electricity price information of the kth day.

[0036] Step 2, construct multifunctional washing machine behavior characteristic prediction model considering weather factors, and predict the behavior characteristics of multifunctional washing machine. The weather factors and electricity price mainly affect the running behavior of multifunctional washing machine, for example, in rainy day, lower temperature and higher humidity, clothes are difficult to dry in outdoor, and the electricity price is lower, at this time, the drying function of washing machine will be increased, the clothes will be dried by multifunctional washing machine, and the drying running power will be increased, so as to change the running behavior of washing machine. The multifunctional washing machine behavior characteristic prediction model constructed in this embodiment is based on BP neural network to train the weather information, electricity price information and the like collected in step 1, so as to predict the behavior characteristics of multifunctional washing machine.

[0037] Step 3, construct multifunctional washing machine load correction model considering weather factors. According to the behavior characteristics of multifunctional washing machine predicted in step 2, the behavior of multifunctional washing machine under different weather information and electricity price information is corrected, and further, the model is constructed to correct the load of multifunctional washing machine.

[0038] Step 4, constructing a multifunctional washing machine load day-ahead adjustable capacity evaluation model considering weather factors. According to the load of the multifunctional washing machine corrected in step 3, a multifunctional washing machine load day-ahead adjustable capacity evaluation model is constructed, in which the influences of weather and electricity price are considered. If it is predicted according to weather and electricity price that the dryer is not used, then the dryer load P i h (t h ) = 0. The multifunctional washing machine load day-ahead adjustable capacity evaluation model includes a maximum up-adjustable capacity evaluation model and a maximum down-adjustable capacity evaluation model, and realizes the evaluation of multifunctional washing machine load day-ahead adjustable capacity.

[0039] In step 2, based on the BP neural network, the outdoor temperature, humidity, weather state and electricity price information are mined, the BP neural network is trained in an offline manner, a multifunctional washing machine behavior feature prediction model based on the BP neural network is constructed, and the day-ahead multifunctional washing machine behavior is effectively predicted. The multifunctional washing machine behavior feature prediction model is shown in Figure 2 , which includes 4 inputs and 2 outputs. The 4 inputs are: temperature T(k) on the kth day, humidity R(k) on the kth day, weather state W(k) on the kth day, and electricity price information ψ(k) on the kth day. The 2 outputs are: δ x = 1 indicates that the predicted day-ahead multifunctional washing machine only starts the washing function, and δ h = 1 indicates that the predicted day-ahead multifunctional washing machine starts the washing function and the drying function. The hidden layer of the BP neural network adopts the tansig function, and the expression of the multifunctional washing machine behavior feature prediction model is:

[0040]

[0041] Wherein, f x () is the washing behavior prediction model of the trained BP neural network, and f h () is the washing and drying behavior prediction model of the trained BP neural network.

[0042] In step 3, the multifunctional washing machine load correction model considering weather factors includes two cases: not using the dryer and using the dryer.

[0043] In the case that the multifunctional washing machine behavior feature prediction model predicts that the dryer is not used, i.e. δ x = 1, then the multifunctional washing machine load is calculated according to expression (4) at this time;

[0044]

[0045] The prediction result of the multi-functional washing machine behavior feature prediction model is that the dryer is not used, i.e., δ h =1, then the load of the multi-functional washing machine is calculated according to expression (6);

[0046]

[0047]

[0048] Where, δ x For laundry behavior predicted based on weather factors and electricity price information, δ x =1 indicates that the multi-functional washing machine will only start the washing function on the predicted date, P(t) is the power of multi-functional washing machine i, δ h δ represents the prediction of washing and drying behavior based on weather factors and electricity price information. h =1 indicates that the multi-functional washing machine activated its washing and drying functions on the predicted date, P i x (t) represents the washing power of the multi-functional washing machine i at time t, P i h (t) represents the drying power of the multi-functional washing machine i at time t.

[0049] This embodiment corrects the load of the multi-functional washing machine based on a multi-functional washing machine load correction model, improving the accuracy of multi-functional washing machine load assessment. The uncorrected and corrected multi-functional washing machine loads are compared. Figure 3 and Figure 4 As shown.

[0050] In step 4, the maximum adjustable capacity assessment model is shown in expressions (7) and (8), where expression (7) represents the earliest possible startup time. For a multi-functional washing machine that starts up before the current time t and after the current time t, the load can be shifted forward to increase the load. The load that can be increased is calculated according to expression (8), and this load is determined by the washing power P. i x (t x ) and drying power P i h (t h It consists of two parts.

[0051] Expression (9) represents the remaining time of the multi-functional washing machine. Greater than the total time required for washing and drying At this time, the load of the multi-functional washing machine can be shifted to the back, thereby reducing the load, i.e., lowering the load. The load that can be lowered is calculated according to expression (10), and this load is determined by the washing power P. i x (t x ) and drying power Pi h (t h ) Two parts. Expression (11) represents when the washing machine behavior predicted according to weather factors and electricity price information is only washing, δ x = 1, let the drying load be 0, that is, P i h (t) = 0; when the washing machine behavior predicted according to weather factors and electricity price information is both washing and drying, δ h = 1, calculate the drying load according to the actual situation, that is, P i h (t) = P i h (t).

[0052]

[0053]

[0054] Where, t x is the washing time, t h is the drying time, ΔP up (t) is the up-regulated load, ΔP dn (t) is the down-regulated load, M is the number of multifunctional washing machines participating in up-regulation, N is the number of multifunctional washing machines participating in down-regulation, is the multifunctional washing machine i1 start-up time participating in up-regulation, is the multifunctional washing machine i1 shutdown time participating in up-regulation, is the multifunctional washing machine i1 drying load end time participating in up-regulation, is the multifunctional washing machine i2 start-up time participating in down-regulation, is the multifunctional washing machine i2 shutdown time participating in down-regulation, is the multifunctional washing machine i2 drying load end time participating in down-regulation, is the washing power of multifunctional washing machine i1 at t time, is the drying power of multifunctional washing machine i1 at t time, is the washing power of multifunctional washing machine i2 at t time, is the drying power of multifunctional washing machine i2 at t time, wherein multifunctional washing machines i1 and i2 belong to i.

[0055] According to the above steps, the effective evaluation of the day-ahead adjustable capacity of multifunctional washing machines can be realized, and the accuracy of adjustable capacity evaluation is improved.

[0056] As used herein, and unless otherwise indicated, all technical and scientific terms have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. As used herein, except where the context demands otherwise, the use of the singular includes the plural. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items. As used herein, the term "or" means and / or. As used herein, the term "comprises the steps of" includes the steps of any combination of the recited steps.

[0057] The above detailed description merely describes the specific implementation of the application. The intention is not to limit the application. Any modifications, equivalent replacements, improvements, and the like made within the spirit and principle of the application shall be included in the protection scope of the application.

Claims

1. A method for evaluating the adjustable load-day capability of a multi-functional washing machine, characterized in that: This includes the following operations: collecting multi-functional laundry load information and weather information; Construct a predictive model for the behavioral characteristics of a multi-functional washing machine that takes weather factors into account, and predict the behavioral characteristics of the multi-functional washing machine. A load correction model for a multi-functional washing machine that takes weather factors into account is constructed. The behavior of the multi-functional washing machine under different weather factors and electricity price information is corrected according to the behavioral characteristics of the multi-functional washing machine, and the corrected load of the multi-functional washing machine is obtained. Based on the modified load of the multi-functional washing machine, a day-ahead adjustable load assessment model for the multi-functional washing machine considering weather factors is constructed, and the day-ahead adjustable load of the multi-functional washing machine is evaluated. The collected multi-functional laundry load operation information includes the washing machine's run time range, washing machine start-up time, washing machine shut-off time, washing machine power, and drying power. The collected weather information includes weather conditions, outdoor temperature, and humidity, expressed as: Among them, X i (k) represents the basic data of the load i of the multi-functional washing machine on day k. The start-up time of the multi-functional washing machine i on day k. The shutdown time of the multi-functional washing machine i on day k. This is the end time of the drying load for the multi-functional washing machine i on day k. Let i be the range of operating time for the multi-functional washing machine on day k. The earliest time that the multi-functional washing machine i can be turned on on day k. P represents the latest time that the multi-functional washing machine i can be turned on on day k. i x P is the washing power of the multi-functional washing machine i. i h Let denot 'i' be the drying power of the multi-functional washing machine i, T(k) be the temperature on day k, R(k) be the humidity on day k, W(k) be the weather condition on day k, with a value of 1 indicating a sunny day, a value of 2 indicating a cloudy day, a value of 3 indicating a rainy day, and a value of 4 indicating a snowy day, and ψ(k) be the electricity price information on day k. The construction of the multi-functional washing machine behavior prediction model taking into account weather factors specifically includes: training the model based on a BP neural network using collected multi-functional washing machine load operation information and weather information, wherein the BP neural network includes 4 inputs and 2 outputs. The 4 inputs are: temperature T(k) on day k, humidity R(k) on day k, weather state W(k) on day k, and electricity price information ψ(k) on day k. The 2 outputs are: δ x =1 indicates that the multi-functional washing machine will only activate the washing function on the predicted date; δ h =1 indicates that the multi-functional washing machine started its washing and drying functions on the predicted date. The hidden layers of the BP neural network use the tansig function. The expression for the multi-functional washing machine behavior feature prediction model is as follows: Among them, f x () represents the trained BP neural network model for predicting laundry behavior, f h () represents the trained BP neural network model for predicting washing and drying behavior; The constructed multi-functional washing machine load correction model, which takes into account weather factors, includes two scenarios: no dryer and dryer. The expression for the load correction model of the multi-functional washing machine without using a dryer is as follows: The expression for the load correction model of a multi-functional washing machine when using a dryer is as follows: Where, δ x For laundry behavior predicted based on weather factors and electricity price information, δ x =1 indicates that the multi-functional washing machine will only start the washing function on the predicted date, P(t) is the power of multi-functional washing machine i, δ h δ represents the prediction of washing and drying behavior based on weather factors and electricity price information. h =1 indicates that the multi-functional washing machine activated its washing and drying functions on the predicted date, P i x (t) represents the washing power of the multi-functional washing machine i at time t, P i h (t) represents the drying power of the multi-functional washing machine i at time t.

2. The method for evaluating the adjustable load day capability of a multi-functional washing machine according to claim 1, characterized in that: The expression for the pre-load adjustable capacity assessment model of the multi-functional washing machine is as follows: Among them, t x For laundry time, t h For drying time, ΔP up (t) represents the load increase, ΔP dn (t) represents the load reduction, M represents the number of washing machines participating in the load increase, and N represents the number of washing machines participating in the load reduction. To participate in the adjustment of the startup time of the i1 multi-functional washing machine, To participate in the increased shutdown time of the i1 multi-functional washing machine, To participate in the adjustment of the drying load end time of the i1 multi-functional washing machine, To participate in the reduction of startup time for the i2 multi-functional washing machine, To participate in the reduction of the shutdown time of the i2 multi-functional washing machine, The drying load end time of the i2 multi-functional washing machine, which is participating in the reduction, The washing power of the multi-functional washing machine i1 at time t. The drying power of the multi-functional washing machine i1 at time t. The washing power of the i2 multi-functional washing machine at time t. Let be the drying power of the multi-functional washing machine i2 at time t, where multi-functional washing machines i1 and i2 belong to i.

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