Electric heating load prediction method

A technology of load forecasting and electric heating, applied in forecasting, neural learning methods, instruments, etc., can solve problems such as the influence of initial value convergence speed, local minimum point, long time, etc., and achieve scientific and reasonable results in maintenance and repair

A technology of load forecasting and electric heating, applied in forecasting, neural learning methods, instruments, etc., can solve problems such as the influence of initial value convergence speed, local minimum point, long time, etc., and achieve scientific and reasonable results in maintenance and repair

CN107909220AInactive Publication Date: 2018-04-13TIANDAQIUSHI ELECTRIC POWER HIGH TECH CO LTD

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  • Electric heating load prediction method
  • Electric heating load prediction method
  • Electric heating load prediction method

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Embodiment Construction

[0053] In order to enable those skilled in the art to better understand the solution of the present invention, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. Apparently, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention. It should be noted that representation and description of components and processes that are not related to the present invention and known to those of ordinary skill in the art are omitted from the drawings and descriptions for the purpose of clarity.

[0054] In describing the present invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", " T...

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Abstract

The invention provides an electric heating load prediction method. The electric heating load prediction method comprises steps that main influence factors of an electric heating load are analyzed; theBP neural network calculation method is constructed; a genetic algorithm is constructed to optimize the BP neural network calculation method; integral data is selected as a training sample and a prediction sample, the BP neural network calculation method and the genetic algorithm are utilized to optimize the BP neural network calculation method to respectively predict an electric heating load, and effectiveness and scientificity of the genetic algorithm for optimizing the BP neural network calculation method are verified. The method is advantaged in that the electric heating load can be precisely predicted, a type of heating equipment of a heating system and a new load size can be accurately determined, heating parameters, idle capacity and a system operating status, etc., can be accurately determined, so operation, maintenance and overhaul of the heating equipment are made to be more scientific and reasonable.

Description

technical field [0001] The invention belongs to the technical field of load forecasting, in particular to an electric heating load forecasting method. Background technique [0002] The heating industry is closely related to people's daily life and plays a decisive role in the development of the entire national economy. In order to reduce energy consumption and reduce urban environmental pollution, most cities in my country currently use centralized heating for heating. Cities and towns in northern my country generally use central heating for heating, and heating energy consumption has become the largest component of building energy consumption. Therefore, heating energy saving is one of the most potential and most effective ways of building energy conservation in my country. [0003] Relying solely on the voluntary behavior of users and enterprises in the central heating system, as well as the mandatory implementation of building energy-saving standards can only play a cert...

Claims

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Application Information

Patent Timeline
13 Apr 2018
Publication
CN107909220A
IPC
G06Q10/04; G06Q50/06; G06N3/04; G06N3/08
CPC
G06N3/084; G06Q10/04; G06Q50/06; G06N3/045
Inventors
李文龙; 严俊