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a |
| 003 - CONTROL NUMBER IDENTIFIER |
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OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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20221228160339.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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221228b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
AIKTC-KRRC |
| Transcribing agency |
AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| 9 (RLIN) |
17501 |
| Author |
Panda, Saroj Kumar |
| 245 ## - TITLE STATEMENT |
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| Title |
Fuzzy Inference Model for Short-Term Load Forecasting |
| 250 ## - EDITION STATEMENT |
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| Volume, Issue number |
Vol, 103(6), Dec |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
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| Place of publication, distribution, etc. |
New York |
| Name of publisher, distributor, etc. |
Springer |
| Year |
2022 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Pagination |
1939--1948p |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
For planning and operation of an energy management system, load forecasting (LF) is essential. For smooth power system operation (PS), LF enhances the energy-efficient and reliable operation. LF also helps to calculate energy supplied by utilities to meet the load plus the energy lost in the PS. Every day, it is necessary to schedule the power generation for the next day. So, short-term load forecasting (STLF) is used to calculate the power dispatch for the next day. In unit commitment, economic allocation of generation and maintenance schedules, STLF is also used. So, to make the STLF more effective, fuzzy logic (FL) is used here. FL is essential for weather-sensitive and historical load data for forecasting the load. The fuzzy decision rule identifies the nonlinear relationship between the input and output data. The historical load and hourly data like temperature, humidity (relative humidity) and wind speed are used for input data. For the training and testing, the hourly based load data are collected from the state load dispatch and communication center of Rajasthan Vidyut Prasaran Nigam, Jaipur (JVN). The triangular membership function of the fuzzy logic model is used to predict the load. The performance of the work is determined by the mean absolute percentage error (MAPE) and the MAPE value for pre-holiday (Saturday), holiday (Sunday), post-holiday, and working day is 0.37%, 0.24%, 0.09%, and 0.09%, respectively. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| 9 (RLIN) |
4623 |
| Topical term or geographic name entry element |
Electrical Engineering |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
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| 9 (RLIN) |
17502 |
| Co-Author |
Ray, Papia |
| 773 0# - HOST ITEM ENTRY |
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| International Standard Serial Number |
2250-2106 |
| Title |
Journal of the institution of engineers (India): Series B |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
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| URL |
https://link.springer.com/article/10.1007/s40031-022-00809-4 |
| Link text |
Click here |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
|
| Koha item type |
Articles Abstract Database |
