CN104537429A - Short-term load forecasting method and device based on data warehouse and data mining technology - Google Patents
Short-term load forecasting method and device based on data warehouse and data mining technology Download PDFInfo
- Publication number
- CN104537429A CN104537429A CN201410757196.9A CN201410757196A CN104537429A CN 104537429 A CN104537429 A CN 104537429A CN 201410757196 A CN201410757196 A CN 201410757196A CN 104537429 A CN104537429 A CN 104537429A
- Authority
- CN
- China
- Prior art keywords
- data
- load
- short
- term
- computing machine
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000005516 engineering process Methods 0.000 title claims abstract description 20
- 238000007418 data mining Methods 0.000 title claims abstract description 19
- 238000013277 forecasting method Methods 0.000 title claims abstract description 9
- 238000004140 cleaning Methods 0.000 claims description 3
- 238000002360 preparation method Methods 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 claims description 2
- 238000012544 monitoring process Methods 0.000 claims description 2
- 238000000034 method Methods 0.000 abstract description 15
- 238000013480 data collection Methods 0.000 abstract 1
- 238000011160 research Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 3
- 238000007726 management method Methods 0.000 description 3
- 238000000605 extraction Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 241000935974 Paralichthys dentatus Species 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000002349 favourable effect Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012067 mathematical method Methods 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000005309 stochastic process Methods 0.000 description 1
Classifications
-
- 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
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2465—Query processing support for facilitating data mining operations in structured databases
-
- 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—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Economics (AREA)
- Theoretical Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- General Physics & Mathematics (AREA)
- Databases & Information Systems (AREA)
- General Business, Economics & Management (AREA)
- Health & Medical Sciences (AREA)
- Tourism & Hospitality (AREA)
- Marketing (AREA)
- General Health & Medical Sciences (AREA)
- Fuzzy Systems (AREA)
- Game Theory and Decision Science (AREA)
- Quality & Reliability (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- Operations Research (AREA)
- Primary Health Care (AREA)
- Development Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Mathematical Physics (AREA)
- Probability & Statistics with Applications (AREA)
- Software Systems (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a short-term load forecasting method based on a data warehouse and a data mining technology. The method is characterized by including the steps of collecting data, extracting regional load historical data, conducting data handling and cleansing, fitting the curvilinear relationship between related parameters and loads, extracting load forecasting data, forecasting loads, and the like. The invention further provides a short-term load forecasting device based on the data warehouse and the data mining technology. The short-term load forecasting device comprises a regional load data collection device and a short-term load forecasting instrument. The method and the device have the advantages that the method is scientific and reasonable, and the short-term loads of power grids can be rapidly forecasted; the device is reasonable in structure, high in automation degree and the like.
Description
Technical field
The present invention relates to fields of measurement, specifically, is a kind of short-term load forecasting method based on Data warehouse and data mining technology and device.
Background technology
Short-term electric load prediction mainly refers to the electric load of forecast coming few hours, 1 day to several days, load forecast is the important component part of energy management system, short-term load forecasting not only for electric system safety, economical operation provides safeguard, and is also the basis of layout operation plan under market environment, power supply plan, trading program.Along with electrical production and the consumption marketization day by day, to the accuracy of load prediction, real-time, reliability with intelligently to have higher requirement, therefore, load prediction has become an important research field in modern power industry system cloud gray model and management.
The size of short-term load forecasting effect depends primarily on precision of prediction, and therefore how improving precision of prediction is the emphasis studying short-term load forecasting Theories and methods at present.Research about short-term load forecasting has very long history, and domestic and international many experts and scholar have done a large amount of research work in prediction theory and method, propose a lot of forecast model.But because the factor affecting short term is a lot, precision of prediction and the usable range of a lot of Forecasting Methodology are restricted.
Therefore, adopt complex mathematical algorithm for current short term prediction method majority, complicated mathematical method is carried out to various data and processes, not only at substantial resource, and the load prediction data obtained is very not satisfactory.
At present, due to the fast development of computer calculate, the Data warehouse and data mining technology especially in database technology, makes based on the data mining in mass data and is treated as possibility.The ability of short-term load forecasting to power network safety operation and raising resisting risk will play very crucial and important effect.
Summary of the invention
Concept of the present invention is:
1. load forecast is the historical data according to electric load and its influence factor, sets up relevant model, carries out scientific forecasting to future electrical energy load.Short term has randomness and uncertainty, this uncertainty just making load prediction tool predict the outcome; Because various load prediction is carried out all under specific conditions, there is conditionality; Short-term load forecasting has regular hour scope, has timeliness; Due to the inaccuracy that predicts the outcome and conditionality, so have multi-scheme;
2. load prediction is the historical summary according to the load past, sets up the load of appropriate mathematical model to future and predicts.Therefore, precision of prediction mainly affects by following factor: historical data; Weather condition; Date type; Forecast model; Social event etc.;
3. short term due to by Changes in weather, social activities and red-letter day the various factors such as type impact, time series shows as the stochastic process of non-stationary, but major part has regularity in each factor of influential system load, thus lay a good foundation for realizing effective prediction.Method at present for short-term load forecasting is a lot, can be divided into classical Forecasting Methodology, traditional prediction method and Intelligent Forecasting etc.The key problem of load forecast research how to utilize existing historical data, set up forecast model, predict future time instance or the load value in the time period, therefore, the reliability of historical data information and forecast model are the principal elements affecting short-term load forecasting precision.Along with the progressively foundation of present power system management infosystem, and the raising of weather forecasting level, the various historical data of Obtaining Accurate is difficulty no longer, and therefore, the key problem of short-term load forecasting is the level height of forecast model;
4. this method is excavated and is implied unknown, to the valuable Repository of decision-making process from mass data, and the knowledge of extraction can be expressed as the forms such as concept, rule, rule and pattern.Data mining process comprises data encasement, knowledge extraction, the representation of knowledge and assessment three phases.In the face of the data message amount that electric system increases day by day, for the requirement of system cloud gray model security and economy, it is very favorable for data mining being used for short-term load forecasting, especially under the condition changed greatly in environmental factor and social information etc., data mining technology in complicated huge history number, can reject mistake and useless data, draws the implicit influence factor determining short term in the data, determine the real Changing Pattern of short term, thus improve precision of prediction;
5. the historical data of electrical network mainly will be comprised network load variable, type variable (week, festivals or holidays on date by the present invention, special event), (temperature, humidity, rainfall amount and special weather are as typhoon for Meteorological Change variable, sandstorm, heavy rain, ice damage), be entered into load prediction historical data computing machine after economic class variable (the GDP value of Grid, the size of population) and other social events (burst social event as Hong Kong accounts for medium) data processing, and logically set up the quick indexing of various factors based on load;
6. in data warehouse, set up the quick indexing between load and above characteristic parameter, when needs carry out short-term load forecasting, input the predicted value of above variable, and GDP is revised according to economic data, after correction factor is added to other events, retrieve relevant data in data warehouse, thus quick and precisely predict electrical network short term.
The present invention is based on above design, provide a kind of scientific and reasonable, can the short-term load forecasting method based on Data warehouse and data mining technology of fast prediction electrical network short term.And provide and realize said method, rational in infrastructure, the device that automaticity is high.
One of the technical solution used in the present invention is: a kind of short-term load forecasting device based on Data warehouse and data mining technology, it is characterized in that, it comprises: region load data gathering-device and short-term load forecasting instrument, described region load data gathering-device comprises electrical network and grid control centre monitoring calculation mechatronics, and grid control centre supervisory control comuter is electrically connected with the USB interface of region load collecting computer, wind gage, temperature humidity measuring instrument, rain sensor are electrically connected with data acquisition board respectively, the USB interface that data acquisition board calculates with region load collection is electrically connected, economic data computing machine is electrically connected with the network interface RJ45 of region load collecting computer, region load collecting computer is electrically connected with demand history data computer, and described region load is collected the USB interface of computing machine, data processing software, data warehouse software and display and is electrically connected successively, described short-term load forecasting instrument comprises: weather bureau's weather forecast computing machine is electrically connected with the network interface RJ45 of short-term load forecasting computing machine, economic data computing machine is electrically connected with the network interface RJ45 of short-term load forecasting computing machine, the data warehouse software of a kind of region load data gathering-device is electrically connected with the network interface RJ45 of short-term load forecasting computing machine by network interface RJ45, the network interface RJ45 of short-term load forecasting computing machine is connected with the data warehouse software be arranged on short-term load forecasting computing machine, the data warehouse software be arranged on short-term load forecasting computing machine is connected with the data mining software be arranged on short-term load forecasting computing machine, the data mining software be arranged on short-term load forecasting computing machine is connected with the load prediction software be arranged on short-term load forecasting computing machine, the load prediction software be arranged on short-term load forecasting computing machine is connected with the display of short-term load forecasting computing machine, the demand history data computer of region load data gathering-device is electrically connected with the network interface RJ45 of short-term load forecasting instrument, and the economic data computing machine of region load data gathering-device is electrically connected with the network interface RJ45 of short-term load forecasting instrument.
Two of the technical solution used in the present invention is: a kind of short-term load forecasting method based on Data warehouse and data mining technology, and it is characterized in that, it comprises the following steps:
1) data acquisition: with region load data gathering-device, network load correlation parameter Real-time Collection is entered region load collecting computer, the network load data of concrete memory space have: network load, week, festivals or holidays, time, temperature, humidity, rainfall amount, typhoon, sandstorm, heavy rain and ice damage special weather, whether GDP value, the size of population of Grid have the accident affecting load, and event category delimited and provided the number percent that affects load stored in basic data needed for the load prediction of regional power grid load data;
2) demand history data in region are extracted: with region load data gathering-device, the data of region load collecting computer collection are extracted to send into be arranged in the data warehouse software of region load collecting computer for every 5 minutes automatically and stores, and send into demand history data computer, the network load data of concrete memory space have: network load, week, festivals or holidays, time, temperature, humidity, rainfall amount, typhoon, sandstorm, heavy rain, ice damage special weather, the GDP value of Grid, whether the size of population has the accident affecting load, event category delimit and provide affect load number percent stored in regional power grid load data load prediction historical data,
3) data preparation, cleaning, matching correlation parameter and load curve relation: the data that step 1) is gathered and step 2) data extracted arrange, revise, clean, remove bad data, ensure that data are effective;
4) load prediction data is extracted: extract load prediction desired data with short-term load forecasting instrument, specifically comprise week, festivals or holidays, temperature, humidity, rainfall amount, typhoon, sandstorm, heavy rain, ice damage special weather, the GDP value of Grid, whether the size of population, have the accident affecting load, and event category delimited and provided the percent data affecting load;
5) load prediction: with short-term load forecasting instrument prediction load desired data, at the data record that the data warehouse retrieval storing load prediction basic data is identical with data, and the load data extracting its correspondence is as predicted load, form the load prediction curve that load is corresponding with the time.
Short-term load forecasting method based on Data warehouse and data mining technology of the present invention is scientific and reasonable, can fast prediction electrical network short term.Apparatus structure of the present invention is reasonable, and automaticity is high.
Accompanying drawing explanation
Fig. 1 is the structural representation of a kind of short-term load forecasting device based on Data warehouse and data mining technology of the present invention;
Fig. 2 predicts the outcome and actual curve comparison diagram.
Embodiment
The invention will be further described to utilize embodiment shown in the drawings below.
With reference to Fig. 1, a kind of short-term load forecasting device based on Data warehouse and data mining technology of the present invention, comprising: region load data gathering-device and short-term load forecasting instrument.Described region load data gathering-device comprises electrical network 1 and is electrically connected with grid control centre supervisory control comuter 5, and grid control centre supervisory control comuter 5 is electrically connected with the USB interface 8 of region load collecting computer 7, wind gage 2, temperature humidity measuring instrument 3, rain sensor 4 are electrically connected with data acquisition board 6 respectively, the USB interface 8 that data acquisition board 6 calculates 7 with region load collection is electrically connected, economic data computing machine 15 is electrically connected with the network interface RJ45 9 of region load collecting computer 7, region load collecting computer 7 is electrically connected with demand history data computer 13, and described region load is collected the USB interface 8 of computing machine 7, data processing software 10, data warehouse software 11 and display 12 and is electrically connected successively, described short-term load forecasting instrument comprises: weather bureau's weather forecast computing machine 14 is electrically connected with the network interface RJ45 17 of short-term load forecasting computing machine 16, economic data computing machine 15 is electrically connected with the network interface RJ45 17 of short-term load forecasting computing machine 16, the network interface RJ45 17 of short-term load forecasting computing machine 16 is connected with the data warehouse software 18 be arranged on short-term load forecasting computing machine, the data warehouse software 18 be arranged on short-term load forecasting computing machine is connected with the data mining software 19 be arranged on short-term load forecasting computing machine, the data mining software 19 be arranged on short-term load forecasting computing machine is connected with the load prediction software 20 be arranged on short-term load forecasting computing machine, be arranged on the load prediction software 20 on short-term load forecasting computing machine to be connected with the display 21 of short-term load forecasting computing machine, the demand history data computer 13 of region load data gathering-device is electrically connected with the network interface RJ45 17 of short-term load forecasting instrument, and the economic data computing machine 15 of region load data gathering-device is electrically connected with the network interface RJ45 17 of short-term load forecasting instrument.
Wind gage, temperature humidity measuring instrument, rain sensor data acquisition unit, computing machine are commercially available prod.Wind gage 2 adopts the precious magnificent AVM-05 wind gage in Taiwan, temperature humidity measuring instrument 3 adopts U.S. fluke Fluke971/F971 temperature humidity measuring instrument, and rain sensor 4 adopts the rain sensor HB333-SL2 of Beijing 1,000,000 electronics, data acquisition board 6 adopts IMP3595 data acquisition system (DAS); Data warehouse software 11 and 19 adopts the PI real-time dataBase system of OSI company of the U.S..
The present invention's data processing software used according to automatic control technology and microcomputer data processing establishment, be those skilled in the art the technology be familiar with.
A kind of short-term load forecasting method based on Data warehouse and data mining technology of the present invention, comprises the following steps:
1) data acquisition: with region load data gathering-device, network load correlation parameter Real-time Collection is entered region load collecting computer, the network load data of concrete memory space have: network load, week, festivals or holidays, time, temperature, humidity, rainfall amount, typhoon, sandstorm, heavy rain and ice damage special weather, whether GDP value, the size of population of Grid have the accident affecting load, event category delimit and provide affect load number percent stored in basic data needed for the load prediction of table 1 regional power grid load data;
Basic data needed for the load prediction of table 1 regional power grid load data
| Sequence number | Project | Time (Hour Minute Second ） | Numerical value |
| 1 | Network load | ||
| 2 | Week | ||
| 3 | Festivals or holidays | ||
| 4 | Temperature | ||
| 5 | Humidity | ||
| 6 | Rainfall amount | ||
| 7 | Special weather | ||
| 8 | The GDP value of Grid | ||
| 9 | The size of population | ||
| 10 | Affect the accident of load |
Region demand history data are extracted: with region load data gathering-device, the data of region load collecting computer collection are extracted to send into be arranged in the data warehouse software of region load collecting computer for every 5 minutes automatically and stores, and send into demand history data computer, the network load data of concrete memory space have: network load, week, festivals or holidays, time, temperature, humidity, rainfall amount, typhoon, sandstorm, heavy rain, ice damage special weather, the GDP value of Grid, whether the size of population has the accident affecting load, event category delimit and provide affect load number percent stored in table 2 regional power grid load data load prediction historical data,
Table 2 regional power grid load data load prediction historical data table
| Sequence number | Project | Time (day Hour Minute Second) | Numerical value |
| 1 | Network load | ||
| 2 | Week | ||
| 3 | Festivals or holidays | ||
| 4 | Temperature | ||
| 5 | Time | ||
| 6 | Humidity | ||
| 7 | Rainfall amount | ||
| 8 | Special weather | ||
| 9 | The GDP value of Grid | ||
| 10 | The size of population | ||
| 11 | Affect the accident of load |
3) data preparation, cleaning, matching correlation parameter and load curve relation: the data that step 1) is gathered and step 2) data extracted arrange, revise, clean, remove bad data, ensure that data are effective;
4) load prediction data is extracted: use short-term load forecasting instrument, extract load prediction desired data, specifically comprise week according to table 2, festivals or holidays, temperature, humidity, rainfall amount, typhoon, sandstorm, heavy rain, ice damage special weather, the GDP value of Grid, the size of population, whether have the accident affecting load, event category delimited and provided the percent data affecting load;
5) load prediction: use short-term load forecasting instrument, according to table 2 load prediction desired data, at the data record that the data warehouse retrieval storing load prediction basic data is identical with data, and the load data extracting its correspondence is as predicted load, form the load prediction curve that load is corresponding with the time.
With reference to Fig. 2, for certain urban distribution network, include 2 years these urban history data, form fundamentals of forecasting data, line correlation of going forward side by side predicts the outcome.
Claims (2)
1. the short-term load forecasting device based on Data warehouse and data mining technology, it is characterized in that, it comprises: region load data gathering-device and short-term load forecasting instrument, described region load data gathering-device comprises electrical network and grid control centre monitoring calculation mechatronics, and grid control centre supervisory control comuter is electrically connected with the USB interface of region load collecting computer, wind gage, temperature humidity measuring instrument, rain sensor are electrically connected with data acquisition board respectively, the USB interface that data acquisition board calculates with region load collection is electrically connected, economic data computing machine is electrically connected with the network interface RJ45 of region load collecting computer, region load collecting computer is electrically connected with demand history data computer, and described region load is collected the USB interface of computing machine, data processing software, data warehouse software and display and is electrically connected successively, described short-term load forecasting instrument comprises: weather bureau's weather forecast computing machine is electrically connected with the network interface RJ45 of short-term load forecasting computing machine, economic data computing machine is electrically connected with the network interface RJ45 of short-term load forecasting computing machine, the data warehouse software of a kind of region load data gathering-device is electrically connected with the network interface RJ45 of short-term load forecasting computing machine by network interface RJ45, the network interface RJ45 of short-term load forecasting computing machine is connected with the data warehouse software be arranged on short-term load forecasting computing machine, the data warehouse software be arranged on short-term load forecasting computing machine is connected with the data mining software be arranged on short-term load forecasting computing machine, the data mining software be arranged on short-term load forecasting computing machine is connected with the load prediction software be arranged on short-term load forecasting computing machine, the load prediction software be arranged on short-term load forecasting computing machine is connected with the display of short-term load forecasting computing machine, the demand history data computer of region load data gathering-device is electrically connected with the network interface RJ45 of short-term load forecasting instrument, and the economic data computing machine of region load data gathering-device is electrically connected with the network interface RJ45 of short-term load forecasting instrument.
2. based on a short-term load forecasting method for Data warehouse and data mining technology, it is characterized in that, it comprises the following steps:
1) data acquisition: with region load data gathering-device, network load correlation parameter Real-time Collection is entered region load collecting computer, the network load data of concrete memory space have: network load, week, festivals or holidays, time, temperature, humidity, rainfall amount, typhoon, sandstorm, heavy rain and ice damage special weather, whether GDP value, the size of population of Grid have the accident affecting load, and event category delimited and provided the number percent that affects load stored in basic data needed for the load prediction of regional power grid load data;
2) demand history data in region are extracted: with region load data gathering-device, the data of region load collecting computer collection are extracted to send into be arranged in the data warehouse software of region load collecting computer for every 5 minutes automatically and stores, and send into demand history data computer, the network load data of concrete memory space have: network load, week, festivals or holidays, time, temperature, humidity, rainfall amount, typhoon, sandstorm, heavy rain, ice damage special weather, the GDP value of Grid, whether the size of population has the accident affecting load, event category delimit and provide affect load number percent stored in regional power grid load data load prediction historical data,
3) data preparation, cleaning, matching correlation parameter and load curve relation: the data that step 1) is gathered and step 2) data extracted arrange, revise, clean, remove bad data, ensure that data are effective;
4) load prediction data is extracted: extract load prediction desired data with short-term load forecasting instrument, specifically comprise week, festivals or holidays, temperature, humidity, rainfall amount, typhoon, sandstorm, heavy rain, ice damage special weather, the GDP value of Grid, whether the size of population, have the accident affecting load, and event category delimited and provided the percent data affecting load;
5) load prediction: with short-term load forecasting instrument prediction load desired data, at the data record that the data warehouse retrieval storing load prediction basic data is identical with data, and the load data extracting its correspondence is as predicted load, form the load prediction curve that load is corresponding with the time.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410757196.9A CN104537429A (en) | 2014-12-11 | 2014-12-11 | Short-term load forecasting method and device based on data warehouse and data mining technology |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410757196.9A CN104537429A (en) | 2014-12-11 | 2014-12-11 | Short-term load forecasting method and device based on data warehouse and data mining technology |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN104537429A true CN104537429A (en) | 2015-04-22 |
Family
ID=52852948
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410757196.9A Pending CN104537429A (en) | 2014-12-11 | 2014-12-11 | Short-term load forecasting method and device based on data warehouse and data mining technology |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104537429A (en) |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104778263A (en) * | 2015-04-23 | 2015-07-15 | 储盈新能源科技（上海）有限公司 | Simulating data mining method for electric vehicle charging station system |
| CN105139295A (en) * | 2015-09-29 | 2015-12-09 | 广东电网有限责任公司电力科学研究院 | Data mining method of mass information of on-line monitoring on power equipment |
| CN105574612A (en) * | 2015-12-14 | 2016-05-11 | 安徽工程大学 | Photovoltaic power generation prediction method based on data mining |
| CN106933956A (en) * | 2017-01-22 | 2017-07-07 | 深圳市华成峰科技有限公司 | Data digging method and device |
| CN107918639A (en) * | 2017-10-19 | 2018-04-17 | 广东电网有限责任公司云浮供电局 | Based on electric power big data main transformer peak load forecasting method and data warehouse |
| CN109862529A (en) * | 2018-12-19 | 2019-06-07 | 河南大学 | Urban Data acquisition system based on wireless sensor network |
| CN109921514A (en) * | 2019-02-01 | 2019-06-21 | 国网浙江省电力有限公司金华供电公司 | Method for power quality controlling decision support |
| CN111177278A (en) * | 2019-12-19 | 2020-05-19 | 绍兴大明电力设计院有限公司 | Grid user short-term load prediction real-time processing tool |
| CN111523715A (en) * | 2020-04-15 | 2020-08-11 | 广东电网有限责任公司 | Comprehensive load prediction method |
| CN114648156A (en) * | 2022-03-02 | 2022-06-21 | 贵州电网有限责任公司 | Time series load prediction method based on convolutional neural network |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100228858A1 (en) * | 2002-12-10 | 2010-09-09 | International Business Machines Corporation | Methods and apparatus for dynamic allocation of servers to a plurality of customers to maximize the revenue of a server farm |
| US20120130768A1 (en) * | 2010-11-19 | 2012-05-24 | Accenture Global Services Limited | Work force planning analytics system |
| CN102567412A (en) * | 2010-12-31 | 2012-07-11 | 上海久隆信息工程有限公司 | Method for processing computer-assisted network load analysis information |
| CN102982387A (en) * | 2012-10-18 | 2013-03-20 | 安徽工程大学 | Method for predicting short-term power load |
| CN103559562A (en) * | 2013-11-20 | 2014-02-05 | 贵州电网公司电力调度控制中心 | Power grid intelligent operation system and achieving method thereof |
| CN103559655A (en) * | 2013-11-15 | 2014-02-05 | 哈尔滨工业大学 | Microgrid novel feeder load prediction method based on data mining |
| CN103606022A (en) * | 2013-12-05 | 2014-02-26 | 国家电网公司 | Short-term load prediction method |
-
2014
- 2014-12-11 CN CN201410757196.9A patent/CN104537429A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100228858A1 (en) * | 2002-12-10 | 2010-09-09 | International Business Machines Corporation | Methods and apparatus for dynamic allocation of servers to a plurality of customers to maximize the revenue of a server farm |
| US20120130768A1 (en) * | 2010-11-19 | 2012-05-24 | Accenture Global Services Limited | Work force planning analytics system |
| CN102567412A (en) * | 2010-12-31 | 2012-07-11 | 上海久隆信息工程有限公司 | Method for processing computer-assisted network load analysis information |
| CN102982387A (en) * | 2012-10-18 | 2013-03-20 | 安徽工程大学 | Method for predicting short-term power load |
| CN103559655A (en) * | 2013-11-15 | 2014-02-05 | 哈尔滨工业大学 | Microgrid novel feeder load prediction method based on data mining |
| CN103559562A (en) * | 2013-11-20 | 2014-02-05 | 贵州电网公司电力调度控制中心 | Power grid intelligent operation system and achieving method thereof |
| CN103606022A (en) * | 2013-12-05 | 2014-02-26 | 国家电网公司 | Short-term load prediction method |
Non-Patent Citations (2)
| Title |
|---|
| 周晨: "基于负荷特性的电力系统短期负荷预测实现", 《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》 * |
| 栗然: "电力负荷分析与预测的分布式数据仓库和数据挖掘研究", 《中国博士学位论文全文数据库信息科技辑》 * |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104778263A (en) * | 2015-04-23 | 2015-07-15 | 储盈新能源科技（上海）有限公司 | Simulating data mining method for electric vehicle charging station system |
| CN104778263B (en) * | 2015-04-23 | 2017-11-03 | 储盈信息科技(上海)有限公司 | A kind of charging station system for electric vehicle emulates data digging method |
| CN105139295A (en) * | 2015-09-29 | 2015-12-09 | 广东电网有限责任公司电力科学研究院 | Data mining method of mass information of on-line monitoring on power equipment |
| CN105574612A (en) * | 2015-12-14 | 2016-05-11 | 安徽工程大学 | Photovoltaic power generation prediction method based on data mining |
| CN106933956A (en) * | 2017-01-22 | 2017-07-07 | 深圳市华成峰科技有限公司 | Data digging method and device |
| CN107918639A (en) * | 2017-10-19 | 2018-04-17 | 广东电网有限责任公司云浮供电局 | Based on electric power big data main transformer peak load forecasting method and data warehouse |
| CN109862529A (en) * | 2018-12-19 | 2019-06-07 | 河南大学 | Urban Data acquisition system based on wireless sensor network |
| CN109921514A (en) * | 2019-02-01 | 2019-06-21 | 国网浙江省电力有限公司金华供电公司 | Method for power quality controlling decision support |
| CN111177278A (en) * | 2019-12-19 | 2020-05-19 | 绍兴大明电力设计院有限公司 | Grid user short-term load prediction real-time processing tool |
| CN111523715A (en) * | 2020-04-15 | 2020-08-11 | 广东电网有限责任公司 | Comprehensive load prediction method |
| CN111523715B (en) * | 2020-04-15 | 2022-04-05 | 广东电网有限责任公司 | Comprehensive load prediction method |
| CN114648156A (en) * | 2022-03-02 | 2022-06-21 | 贵州电网有限责任公司 | Time series load prediction method based on convolutional neural network |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104537429A (en) | Short-term load forecasting method and device based on data warehouse and data mining technology | |
| Dong et al. | Wind power day-ahead prediction with cluster analysis of NWP | |
| CN108593990B (en) | Electricity stealing detection method based on electricity consumption behavior mode of electric energy user and application | |
| CN109726872B (en) | Energy consumption prediction method and device, storage medium and electronic equipment | |
| CN111178758A (en) | Concrete dam monitoring data intelligent management and real-time evaluation system based on BIM | |
| CN103854068A (en) | Method for forecasting residential quarter short-term loads | |
| CN104808587A (en) | Utilization statistical approach based on operation states of machining equipment | |
| CN103927694A (en) | Real-time analysis and decision system of regional loads of urban power grid and working method thereof | |
| CN102004859A (en) | On-line diagnostic method for abnormal energy consumption branch of building | |
| CN101799674A (en) | Method for analyzing service state of numerical control equipment | |
| CN103500364B (en) | Energy quality steady-state index prediction method and system | |
| CN102509173A (en) | Markov chain based method for accurately forecasting power system loads | |
| CN104638636A (en) | Power daily load characteristic indicator prediction method | |
| CN106405280B (en) | A kind of intelligent substation on-line monitoring parameter trend method for early warning | |
| CN111008727A (en) | Power distribution station load prediction method and device | |
| CN101976301A (en) | Method and device for preprocessing historical data in yearly load forecasting | |
| CN106600029A (en) | Macro-economy predictive quantization correction method based on electric power data | |
| CN110673081B (en) | Intelligent electric meter error online estimation method based on edge calculation | |
| CN102175202A (en) | Ice cover thickness predicting method based on fuzzy logic | |
| CN116166655B (en) | Big data cleaning system | |
| CN114184870A (en) | Non-invasive load identification method and equipment | |
| CN103501003A (en) | Line loss control system | |
| Lin et al. | The data mining application in the power quality monitoring data analysis | |
| CN103325066A (en) | Network load forecasting management system | |
| CN111177278A (en) | Grid user short-term load prediction real-time processing tool |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WD01 | Invention patent application deemed withdrawn after publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20150422 |