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Power load short-term interval prediction method, device, equipment and medium
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A power load and short-term technology, applied in the field of power forecasting, can solve problems such as inability to accurately respond to load fluctuation ranges, and does not consider the impact of load trends, achieving high accuracy and improving economic efficiency
Pending Publication Date: 2022-02-18
HUNAN UNIV
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[0003] Most of the existing traditional load forecasting methods are researched on point forecasting. Point forecasting can only reflect the change trend of load, and cannot accurately reflect the possible fluctuation range of load.
In fact, power system load forecasting contains various uncertain factors, which makes the decision-making of power system dispatching have certain risks.
Moreover, it is difficult to ensure the correctness and safety of power system dispatching decisions only from point prediction results.
In the traditional short-term load forecasting method, although weather factors, historical data, etc. are considered, the influence of load trend is not considered
Method used
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Embodiment 1
[0042] This embodiment is based on the GWO-ALO-ELM short-term interval forecasting method for electric load, refer to Figure 1-2 shown, including the following steps:
[0043] Step 1. Obtain the power load and temperature and humidity data spanning at least one year and at preset intervals in the historical period, and record the type of week of the day corresponding to each data time, that is, the data that each data belongs to on the day of the week; use the obtained The data constructs several sets of raw data, and each set of raw data is expressed as x t =(P l,t-n ,P l,t-1 , T t , T t max , T t min , T tmean , Hu t ,D t ,P l,t ); among them, P l,t-n ,P l,t-1 ,P l,t Respectively represent the power load at time t-n, t-1, t, T t Indicates the temperature at time t, T t max , T t min , T tmean Respectively represent the highest temperature, the lowest temperature and the average temperature at all times of the day at time t, Hu t Indicates the humidity at ti...
Embodiment 2
[0112] This embodiment provides a short-term interval forecasting device for electric load based on GWO-ALO-ELM, including:
[0113] The original data acquisition module is used to: acquire the power load and temperature and humidity data at preset intervals in the historical period, and construct several sets of original data, and each set of original data is expressed as x t =(P l,t-n ,P l,t-1 , T t , T t max , T t min , T tmean , Hu t ,D t ,P l,t ); among them, P l,t-n ,P l,t-1 ,P l,t Respectively represent the power load at time t-n, t-1, t, T t Indicates the temperature at time t, T t max , T t min , T tmean Respectively represent the highest temperature, the lowest temperature and the average temperature at all times of the day at time t, Hu t Indicates the humidity at time t, D t Indicates which day of the week the moment t belongs to; n is the number of moments included in a day;
[0114] The data preprocessing module is used for: normalizing the data ...
Embodiment 3
[0121] This embodiment provides an electronic device, including a memory and a processor, where a computer program is stored in the memory, and when the computer program is executed by the processor, the processor implements the method described in Embodiment 1.
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Abstract
The invention discloses a power load short-term interval prediction method, device, equipment and medium, and the method comprises the steps: obtaining historical power load and temperature and humidity data, carrying out the preprocessing, and constructing a training sample, wherein the upper limit and the lower limit of the power load interval in the training sample are obtained by superposing random white noise obeying normal distribution on the power load; optimizing the initial population of the ant lion algorithm ALO by using a grey wolf algorithm GWO to obtain the optimal initial population of the ALO; and constructing a power load short-term interval prediction model based on the GWO-ALO-ELM by using the input weight of the ALO optimization ELM under the initial population and the optimization of the implicit strata bias, so as to carry out short-term interval prediction on the power load at the target moment. According to the method, the influence factors of the load are finely considered, the interval prediction of the power load can be realized, the accuracy is high, and the method has certain guiding significance for dispatching decision making, planning and the like of a power system.
Description
technical field [0001] The present invention is applied in the field of electric power forecasting, and specifically refers to a short-term interval forecasting method, device, equipment and medium of electric load based on GWO-ALO-ELM. Background technique [0002] Power load forecasting has an important impact on reasonable grid planning and healthy grid operating environment. Through comprehensive and detailed research on load forecasting, it is beneficial to formulate efficient and economical power generation plans and arrange units reasonably, so as to provide users with safe and reliable power supply, ensure the safe and stable operation of the power system, reduce power generation costs, and improve economic efficiency. benefit. Short-term load forecasting not only provides guarantee for the safe and economical operation of the power system, but also is the basis for dispatching, power supply and trading plans in the market environment. [0003] Most of the existing...
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