KR20210078290A - Data Clustering Method for Energy Demand Management in Smart Energy Cities - Google Patents

Data Clustering Method for Energy Demand Management in Smart Energy Cities Download PDF

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KR20210078290A
KR20210078290A KR1020190170161A KR20190170161A KR20210078290A KR 20210078290 A KR20210078290 A KR 20210078290A KR 1020190170161 A KR1020190170161 A KR 1020190170161A KR 20190170161 A KR20190170161 A KR 20190170161A KR 20210078290 A KR20210078290 A KR 20210078290A
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South Korea
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energy
cities
clustering method
smart
demand management
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KR1020190170161A
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Korean (ko)
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박세현
윤구원
박상욱
박상민
최명인
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중앙대학교 산학협력단
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Publication of KR20210078290A publication Critical patent/KR20210078290A/en

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/906—Clustering; Classification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management

Abstract

According to the present invention, disclosed is a data clustering method for energy demand management in smart energy cities. Therefore, the method reduces energy usage costs while efficiently using energy.

Description

스마트 에너지 시티 내 에너지 수요관리를 위한 데이터 클러스터링 방법{Data Clustering Method for Energy Demand Management in Smart Energy Cities}Data Clustering Method for Energy Demand Management in Smart Energy Cities

본 발명은 스마트 에너지 시티 내 에너지 수요관리를 위한 데이터 클러스터링 방법에 관한 것이다. The present invention relates to a data clustering method for energy demand management in a smart energy city.

최근 세계적으로 급격한 도시화, 산업화로 현대인의 기본적인 생활 패턴의 변화로 인해 도시 경제 경쟁력 위한 대책이 필요하다. 또한, 도시 인구의 증가 추세와 도시 환경의 통제 개념의 지속적인 변화로 인해 도시의 복잡한 시스템을 스마트하게 관리하고 지원할 수 있는 통합 및 상호 운용 시스템의 필요성이 증가하고 있다. 그리하여, 첨단 Information and Communications Technologies (ICT)기반으로 하는 제4차 산업혁명 및 초연결시대로 인해 우리의 도시 환경은 빠르게 변화하며 스마트 시티가 구현되고 있다. Due to changes in the basic living patterns of modern people due to rapid urbanization and industrialization worldwide, measures for urban economic competitiveness are needed. In addition, due to the growing trend of urban population and the continuous change in the concept of control of the urban environment, the need for integrated and interoperable systems that can smartly manage and support the complex systems of cities is increasing. Therefore, our urban environment is rapidly changing due to the 4th industrial revolution and hyper-connected age based on advanced Information and Communications Technologies (ICT), and smart cities are being implemented.

클러스터링 방법은 인공지능의 비지도 학습의 일부분이다. 비지도 학습 후 지도학습을 할 경우 보다 큰 파급효과를 얻을 수 있으나 현재 아직 많은 기술 개발이 되지 않아 어려움을 겪고 있다. Clustering methods are part of unsupervised learning in artificial intelligence. If supervised learning is performed after unsupervised learning, a greater ripple effect can be obtained, but it is currently experiencing difficulties because many technologies have not yet been developed.

종래 기술의 문제점을 해결하기 위해 본 발명은 인공지능의 비지도 학습의 일부 인 K-means 클러스터링 알고리즘을 활용을 하여 스마트 에너지 시티 내 에너지 수요관리를 하여 효율적인 에너지 사용을 하면서 에너지 사용 요금을 절감한다.In order to solve the problems of the prior art, the present invention utilizes the K-means clustering algorithm, which is a part of unsupervised learning of artificial intelligence, to manage energy demand in a smart energy city to reduce energy usage charges while efficiently using energy.

상기한 바와 같은 목적을 달성하기 위하여, 본 발명의 일 실시예에 따르면, 스마트 에너지 시티 내 에너지 수요관리를 위한 데이터 클러스터링 방법이 제공된다. In order to achieve the above object, according to an embodiment of the present invention, a data clustering method for energy demand management in a smart energy city is provided.

본 발명에 따르면, 일기예보, 인구증가량, 전력사용 데이터를 인공지능의 비지도 학습의 K-means 클러스터링 알고리즘을 활용하여 과거의 데이터를 기반으로 하여 미래의 에너지를 최적의 에너지 상태로 수행할 수 있다. According to the present invention, future energy can be performed in an optimal energy state based on past data by using K-means clustering algorithm of artificial intelligence unsupervised learning for weather forecast, population growth, and electricity use data. .

도 1은 스마트 에너지 시티 내 에너지 수요관리를 위한 데이터 클러스터링 방법의 전체 구성도이다.
도 2은 본 발명의 데이터의 종류를 데이터베이스를 나타낸다.
도 3은 본 발명의 클러스터링 기반의 관리프로세스를 나타낸다.1 is an overall configuration diagram of a data clustering method for energy demand management in a smart energy city.
2 shows a database of types of data according to the present invention.
3 shows a clustering-based management process of the present invention.

본 발명은 다양한 변경을 가할 수 있고 여러 가지 실시예를 가질 수 있는 바, 특정 실시예들을 도면에 예시하고 상세하게 설명하고자 한다.Since the present invention can have various changes and can have various embodiments, specific embodiments are illustrated in the drawings and described in detail.

그러나, 이는 본 발명을 특정한 실시 형태에 대해 한정하려는 것이 아니며, 본 발명의 사상 및 기술 범위에 포함되는 모든 변경, 균등물 내지 대체물을 포함하는 것으로 이해되어야 한다. However, this is not intended to limit the present invention to specific embodiments, and it should be understood to include all modifications, equivalents and substitutes included in the spirit and scope of the present invention.

본 발명은 인공지능의 비지도 학습의 일부 인 K-means 클러스터링 알고리즘을 활용을 하여 스마트 에너지 시티 내 에너지 수요관리를 하여 효율적인 에너지 사용을 하면서 에너지 사용 요금을 절감한다.The present invention utilizes the K-means clustering algorithm, which is a part of unsupervised learning of artificial intelligence, to manage energy demand in a smart energy city to reduce energy usage charges while efficiently using energy.

도 1에서는 본 발명에서 제안하는 스마트 에너지 시티 내 에너지 수요관리를 위한 데이터 클러스터링 방법의 구성도이다. 방법은 크게 데이터베이스(100), 관리프로세스(200)의 2가지로 구성됩니다.1 is a block diagram of a data clustering method for energy demand management in a smart energy city proposed by the present invention. The method is mainly composed of two types of database (100) and management process (200).

도 2는 평균 기온, 인구증가, 전력소비 데이터를 데이터베이스를 저장을 하여 과거의 데이터를 구축한다. 2 is a database of average temperature, population growth, and power consumption data to build past data.

도 3는 데이터 베이스에 있는 데이터를 가져와 K-Means 알고리즘을 활용을 하여 데이터들을 분석을 하여 하나의 프로세스를 형성한다. 관리 프로세스에는 소비분석, 효율성 분석, 예측 분석, 전력 소비 원인 분석을 하여 과거의 에너지를 분석을 하여 미래의 에너지를 최적의 에너지 상태로 수행할 수 있다. 3 shows data in the database and analyzes the data using the K-Means algorithm to form a process. In the management process, consumption analysis, efficiency analysis, predictive analysis, and power consumption cause analysis are performed to analyze the energy of the past, and the energy of the future can be performed in an optimal energy state.

상기한 본 발명의 실시예는 예시의 목적을 위해 개시된 것이고, 본 발명에 대한 통상의 지식을 가지는 당업자라면 본 발명의 사상과 범위 안에서 다양한 수정, 변경, 부가가 가능할 것이며, 이러한 수정, 변경 및 부가는 하기의 특허청구범위에 속하는 것으로 보아야 할 것이다.The above-described embodiments of the present invention have been disclosed for the purpose of illustration, and various modifications, changes, and additions will be possible within the spirit and scope of the present invention by those skilled in the art having ordinary knowledge of the present invention, and such modifications, changes and additions should be regarded as belonging to the following claims.

Claims (1)

스마트 에너지 시티 내 에너지 수요관리를 위한 데이터 클러스터링 방법.

A data clustering method for energy demand management in a smart energy city.
KR1020190170161A 2019-12-18 2019-12-18 Data Clustering Method for Energy Demand Management in Smart Energy Cities KR20210078290A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114103707A (en) * 2021-12-06 2022-03-01 黄淮学院 Intelligent energy control method and system based on artificial intelligence and Internet of things

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114103707A (en) * 2021-12-06 2022-03-01 黄淮学院 Intelligent energy control method and system based on artificial intelligence and Internet of things
CN114103707B (en) * 2021-12-06 2024-01-26 黄淮学院 Intelligent energy control method and system based on artificial intelligence and Internet of things

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