Method for predicting energy consumption of buildings during holidays and festivals on basis of time series and neural networks

A technology of time series and building energy consumption, which is applied in forecasting, data processing applications, calculations, etc., can solve problems such as not considering holidays, unsatisfactory energy consumption prediction results, and incomplete factors affecting building energy consumption

Inactive Publication Date: 2014-12-24
刘岩
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Problems solved by technology

The above methods simulate and predict the energy consumption of specific buildings. The results show that the energy consumption prediction applied to normal days is feasible, and the prediction errors are all within the all

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  • Method for predicting energy consumption of buildings during holidays and festivals on basis of time series and neural networks
  • Method for predicting energy consumption of buildings during holidays and festivals on basis of time series and neural networks
  • Method for predicting energy consumption of buildings during holidays and festivals on basis of time series and neural networks

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Embodiment Construction

[0018] Combined with specific examples below, refer to figure 1 The specific embodiment of the method of the present invention is described in detail.

[0019] Step 1: Collect data and perform data preprocessing

[0020] This embodiment is a certain office building in Shenzhen City. It collects the daily power consumption data of the building (the required time is at least 2 years), and the holiday information, daily average temperature, and humidity data during this period of time, and calculates the daily power consumption data. The stationarity test of the electricity time series was carried out. If the time series is not stable, the sample sequence is adjusted by difference operation to eliminate its trend and seasonality, so that the changed sequence is a stationary sequence. Let the original daily power consumption time data series be {X t}(t=1,2,…,N), generally, for the non-stationary time series of daily power consumption of buildings with a period of s, it can be t...

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Abstract

The invention provides a method for predicting energy consumption of buildings during holidays and festivals on the basis of time series and neural networks. The method essentially includes predicting energy consumption of the buildings by means of fitting by the aid of the time series; solving prediction errors of energy consumption of the buildings during holidays and festivals; simulating the neural networks by the aid of influence factors on the energy consumption of the buildings during holidays and festivals and the solved prediction errors; computing modification values of the energy consumption of the buildings during holidays and festivals; modifying prediction results of the energy consumption of the buildings during holidays and festivals. The method has the advantages that the energy consumption of the buildings during holidays and festivals can be predicted, and the prediction precision can be improved to a great extent.

Description

technical field [0001] The invention relates to a prediction method of building energy consumption, which belongs to the field of building energy consumption prediction, in particular to a method for predicting building energy consumption during holidays and holidays based on time series and neural networks. Background technique [0002] With the development of my country's economy, the problem of high energy consumption in office buildings and large public buildings has become increasingly prominent. Doing a good job in their energy conservation management is of great significance to the realization of the "Twelfth Five-Year Plan" building energy conservation planning goals. Building energy conservation is the frontier and research hotspot of today's urban construction and social development. Comprehensive analysis and evaluation of the current energy consumption of buildings is the premise and basis for building energy conservation. Establishing a relatively simple and accu...

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Application Information

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IPC IPC(8): G06Q10/04
Inventor 牛丽仙吴忠宏刘岩
Owner 刘岩
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