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Monthly electricity sales predication method taking regard of comfortable temperature and random change influence
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A comfortable temperature and prediction method technology, applied in data processing applications, instruments, calculations, etc., can solve problems such as low prediction accuracy
Inactive Publication Date: 2016-08-10
STATE GRID CORP OF CHINA +2
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In view of the problem that the conventional linear regression model for monthly electricity sales forecast ignores the existence of a comfortable temperature range and the influence of random changes, the prediction accuracy may not be high. Two corresponding improvement measures are proposed: choose the low temperature threshold temperature and the high temperature valve When the actual temperature is lower than the low-temperature threshold temperature or higher than the high-temperature threshold temperature, heating or cooling measures will be taken; use the "random change level" to quantify the random change, and use its quantified value as the monthly electricity sales impact Factors included in the regression forecasting model of monthly electricity sales
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Embodiment 1
[0054] combined with figure 1 , the monthly electricity sales forecast linear regression model considering the influence of comfort temperature and random fluctuations includes the following steps:
[0055] I) Historical data collection, which mainly includes monthly electricity sales, daily electricity sales, and daily maximum and minimum temperatures for consecutive years before the required forecast month.
[0056] II) Historical data sorting and parameter determination
[0057] 1) Consider the monthly heating coefficient and cooling coefficient of the comfortable temperature range
[0058] Firstly, the low-temperature threshold temperature and high-temperature threshold temperature are determined from the relationship between daily electricity sales and daily average temperature, and the improved method considering the influence of temperature on monthly electricity sales is to select the low-temperature threshold temperature and high-temperature threshold temperature res...
Embodiment 2
[0086] Forecast the monthly electricity sales in X region of Chongqing in 2014 (modeling is always based on the data of the first 4 years of the forecast month). Taking the forecast of January 2014 as an example to elaborate the forecasting process, the forecasting methods for other months are similar.
[0087] I) Historical data collection
[0088] Collect monthly electricity sales, daily electricity sales, and daily maximum and minimum temperatures for 48 months from January 2010 to December 2013.
[0090] First, the low temperature threshold temperature and the high temperature threshold temperature are determined from the relationship between the daily electricity sales in X area and the daily average temperature, which are 13°C and 28°C respectively, and according to the formula (1) ~ formula (2) in the technical plan Obtain the improved heating coefficient and improved cooling coefficient for each month of 48 months; secondly, sort out the t...
Embodiment 3
[0103] For verifying the effectiveness of the present invention, design following comparison scheme:
[0104] (1) Option 1: A conventional linear regression model for forecasting monthly electricity sales, that is, model (1-1).
[0105] (2) Scheme 2: The conventional linear regression model for monthly electricity sales forecast only considers the improvement of the influence of temperature on monthly electricity sales.
[0106] (3) Scheme 3: A linear regression model for forecasting monthly electricity sales considering the influence of temperature and random variables, that is, model (4).
[0107] The prediction results of the above three schemes are shown in Table 1.
[0108] Table 1 The forecast results of monthly electricity sales in 2014
[0109]
[0110] It can be seen from Table 1 that:
[0111] (1) From the point of view of monthly forecast error, the difference between the forecast errors of Scheme 1 and Scheme 2 is small; from the perspective of average foreca...
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Abstract
The invention discloses a monthly electricity sales predication method taking regard of comfortable temperature and random change influence. Firstly, existence of a comfortable temperature range is considered, namely a low threshold temperature and a high threshold temperature are selected. When the actual temperature is lower than the low threshold temperature or higher than the high threshold temperature, a heating measure or a cooling measure is taken, and therefore a monthly heating coefficient and a heating coefficient of a monthly electricity sales predication linear regression model are corrected. Secondly, the random change influence is considered; a random change grade is brought forward for quantifying the random change; and furthermore the quantified value is used as a monthly electricity sales influence factor and is substituted into the monthly electricity sales predication linear regression model. Finally, two improvement measures are considered based on a routine monthly electricity sales predication linear regression model, thereby forming the monthly electricity sales predication linear regression model taking regard of the comfortable temperature and the random change influence. The monthly electricity sales predication method have advantages of better establishing the relation between the temperature and the monthly electricity sales, reasonably considering the influence of the random change to the monthly electricity sales, and improving prediction precision for the monthly electricity sales.
Description
technical field [0001] The invention relates to the technical field of power system electricity forecasting, in particular to a method for predicting monthly electricity sales in an electric powersystem, and in particular to a method for predicting monthly electricity sales considering the influence of comfortable temperature and random fluctuations. Background technique [0002] Monthly electricity sales forecast refers to estimating or expressing future monthly electricity sales by means of sorting out and analyzing historical data. Whether it is the peer-to-peer benchmarking assessment system of the State Grid Corporation of China, or the power marketing work in the power market environment, the monthly electricity sales forecast is one of the important contents. How to improve the prediction accuracy of monthly electricity sales is an important issue that power companies are very concerned about and devoted to research. Its role mainly includes the following aspects: ⑴M...
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