AI SearchElectricity price forecasting in smart grid using machine learning (Record no. 36000)
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| 005 - DATE AND TIME OF LATEST TRANSACTION | |
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| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | .b1238558x |
| 099 #9 - LOCAL FREE-TEXT CALL NUMBER (OCLC) | |
| Classification number | AIT Diss. no.ET-21-01 |
| 100 0# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Pornchai Chaweewat |
| 245 10 - TITLE STATEMENT | |
| Title | Electricity price forecasting in smart grid using machine learning |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Pathum Thani, Thailand : |
| Name of publisher, distributor, etc. | Asian Institute of Technology, |
| Date of publication, distribution, etc. | 2021 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 136 leaves : |
| Other physical details | ill. |
| 490 1# - SERIES STATEMENT | |
| Series statement | Dissertation ; |
| Volume/sequential designation | no. ET-21-01 |
| 500 ## - GENERAL NOTE | |
| General note | A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Engineering in Energy, School of Environment, Resources and Development |
| 502 ## - DISSERTATION NOTE | |
| Dissertation note | Thesis (Ph.D.) - Asian Institute of Technology, 2021 |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | At present, electricity price forecasts have become a fundamental input to energy utility and company for decision-making mechanisms. Electricity price forecasting focuses on predicting the spot and forward prices in the wholesale electricity market. The electricity price forecasting depends on weather and the intensity of business and everyday activities. These unique characteristics lead to price dynamics and price spikes. For these reasons, electricity price forecasting is a big challenge in the energy market. A variety of methods and ideas have been developed with varying degrees of success. Computational intelligence models or call machine learning techniques gain popularity in recent years because their major strength is the ability to handle complexity and non-linearity, like electricity price volatility. Therefore, this study will focus on modeling electricity price forecasting based on machine learning techniques with real data simulation. In this work, to examine electricity price forecasting, the proposed electricity forecasting models were formulated based on conventional and modern machine learning techniques. The novel residual neural network in electricity price forecasting was first introduced in this study. The simulation results were evaluated using the coverage width-base criterion, which showed that the proposed model could improve forecasting results accurately and reliable. The average forecasting error was reduced by about 13% error with comparing to multilayer perceptron models. Moreover, this study tried to reduce forecasting error by improving electricity demand and renewable energy resource forecasting. The proposed forecasting model cooperated with demand and renewable energy resource forecasts. The results were reduced below 1%, while the benchmark models are around 5% error. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Electric utilities |
| General subdivision | Rates |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Smart power grids |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Electricity |
| General subdivision | Marketing |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Machine learning |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Singh, Jai Govind, |
| Relator term | Chairperson |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Weerakorn Ongsakul, |
| Relator term | Examination Committee |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Dhakal, Shobhakar, |
| Relator term | Examination Committee |
| 710 2# - ADDED ENTRY--CORPORATE NAME | |
| Corporate name or jurisdiction name as entry element | PEA-AIT Education Cooperation Project, |
| Relator term | Scholarship Donor |
| 710 2# - ADDED ENTRY--CORPORATE NAME | |
| Corporate name or jurisdiction name as entry element | Royal Thai Government Fellowship, |
| Relator term | Scholarship Donor |
| 810 2# - SERIES ADDED ENTRY--CORPORATE NAME | |
| Corporate name or jurisdiction name as entry element | Asian Institute of Technology. |
| Title of a work | Dissertation ; |
| Volume/sequential designation | no. ET-21-01 |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Materials specified | Full-Text |
| Uniform Resource Identifier | <a href="http://203.159.5.9/ait-thesis/detail.php?q=B16667">http://203.159.5.9/ait-thesis/detail.php?q=B16667</a> |
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| Withdrawn status | Lost status | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Total checkouts | Full call number | Barcode | Date last seen | Copy number | Price effective from | Koha item type | Cost, normal purchase price |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Available for Loans | Asian Institute of Technology Library | Asian Institute of Technology Library | Archives | 18/08/2026 | AIT Diss. no.ET-21-01 | 30020220006335 | 18/08/2026 | 1 | 18/08/2026 | 40-Archives | |||||
| Available for Loans | Asian Institute of Technology Library | Asian Institute of Technology Library | Archives | 18/08/2026 | AIT Diss. no.ET-21-01 | 18/08/2026 | 18/08/2026 | 61-CD-ROM | |||||||
| Available for Loans | Asian Institute of Technology Library | Asian Institute of Technology Library | AIT Publications | 18/08/2026 | AIT Diss. no.ET-21-01 | 30050211007520 | 18/08/2026 | 1 | 18/08/2026 | 20-AIT Publication | 50.00 | ||||
| Available for Loans | Asian Institute of Technology Library | Asian Institute of Technology Library | AIT Publications | 18/08/2026 | AIT Diss. no.ET-21-01 | 30050211007512 | 18/08/2026 | 2 | 18/08/2026 | 20-AIT Publication | 50.00 |