A short-term load prediction method considering somatosensory temperature and radiation intensity

A technology for short-term load prediction and somatosensory temperature, applied in data processing applications, instruments, computing, etc., can solve problems that have not been effectively solved, and achieve the effect of reducing neural network dimensions and improving training efficiency

Active Publication Date: 2019-06-14
NARI TECH CO LTD +4
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Problems solved by technology

However, how to comprehensively consider meteorological factors such as temperature and humidity and th

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  • A short-term load prediction method considering somatosensory temperature and radiation intensity
  • A short-term load prediction method considering somatosensory temperature and radiation intensity
  • A short-term load prediction method considering somatosensory temperature and radiation intensity

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[0049] The present invention will be further described below in conjunction with the accompanying drawings.

[0050] like figure 1 As shown, an implementation case of the present invention includes adopting the method of the present invention, and its characteristics, purpose and advantages can be seen from the steps of the embodiment in the process of short-term load forecasting.

[0051] A short-term load forecasting method considering body temperature and radiation intensity, which specifically includes the following steps:

[0052] Step 1: Query the daily load and meteorological and daily type information of historical dates as sample data. The sample data mainly includes: daily 96-point load data, daily 24-hour meteorological data and daily type information in the past five years.

[0053] Step 2: Calculate the 24-hour historical somatosensory temperature data, daily maximum load and daily minimum load of historical dates. Historical somatosensory temperature T g Calc...

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Abstract

The invention discloses a short-term load prediction method considering somatosensory temperature and radiation intensity. The short-term load prediction method comprises the following steps of 1) inquiring sample data such as historical load and weather; 2) calculating historical somatosensory temperature data and a daily load level; 3) selecting an optimal'mode similar day 'from the historical sample data set on the basis of the day type information and meteorological data of the to-be-predicted day, and finally calculating to obtain a normalized load curve; 4) establishing a neural networkprediction model considering the somatosensory temperature and the sunlight intensity to obtain the load level of the to-be-predicted day; and 5) calculating load data of the to-be-predicted day through the normalization curve and the load level. According to the method, the influence on the somatosensory temperature of the load and the sunlight intensity of the distributed photovoltaic power generation are fully considered, the change rule of the historical load is fully considered, the load level and the load mode are separately predicted, the input dimension of the neural network is reduced, the network training load is reduced, and the calculation efficiency is improved.

Description

technical field [0001] The invention relates to a short-term load forecasting method considering body temperature and radiation intensity, and belongs to the technical field of power system load forecasting. Background technique [0002] At present, short-term load forecasting is the basis for power grid companies to prepare power generation plans and carry out real-time operation control work. Through accurate load forecasting, it is possible to economically arrange the start-up and shutdown of generating units, and reasonably arrange planned maintenance of generating units, ensuring the stability and reliability of the power grid While supplying power, it can effectively reduce the cost of power generation and improve the overall economic benefits of society. [0003] With the continuous improvement of residents' living standards and quality of life, the proportion of residential electricity load in the grid load is also increasing, the most obvious is summer air condition...

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Application Information

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IPC IPC(8): G06Q10/06G06Q50/06
Inventor 喻乐张珂珩张晶施磊谢旭涂孟夫史佩然耿琳宁健沈茂亚
Owner NARI TECH CO LTD
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