Monthly electricity demand forecast for Provincial Electricity Authority using autoregressive integreted moving average (ARIMA) and artificial neural network (ANN): (Record no. 78508)

MARC details
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005 - DATE AND TIME OF LATEST TRANSACTION
control field 20260818155749.0
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fixed length control field 130314s2011 th uu|m rtt 0| a1eng d
035 ## - SYSTEM CONTROL NUMBER
System control number .b12116166
099 #9 - LOCAL FREE-TEXT CALL NUMBER (OCLC)
Classification number AIT Thesis no.ET-11-24
100 0# - MAIN ENTRY--PERSONAL NAME
Personal name Chonlapat Leewarinpanich
245 10 - TITLE STATEMENT
Title Monthly electricity demand forecast for Provincial Electricity Authority using autoregressive integreted moving average (ARIMA) and artificial neural network (ANN):
Remainder of title a case study of Chiangmai
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. 2011
300 ## - PHYSICAL DESCRIPTION
Extent 66 leaves :
Other physical details ill. +
Accompanying material 1 online resource
490 1# - SERIES STATEMENT
Series statement Thesis ;
Volume/sequential designation no. ET-11-24
500 ## - GENERAL NOTE
General note A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Energy
502 ## - DISSERTATION NOTE
Dissertation note Thesis (M.Sc.) - Asian Institute of Technology, 2011
520 ## - SUMMARY, ETC.
Summary, etc. This study analyzes and forecasts monthly electricity demand for Provincial Electricity Authority in Chingmai with two approaches. They are Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN). The study focuses on monthly historical data of electricity consumption from 2003 to 2010. ARIMA method used in the study will use SPSS software. Artificial Neural Network (ANN) approach applied the multi - layer perceptron and backpropagation algorithm and MATLAB program has been used for the study. This study involves not only past electricity consumption pattern, but also local socio - economic and climatic factors influencing electricity demand in the model such as monthly average temperature, monthly average maximum temperature, monthly average minimum temperature, number of customers and Ft charge. According to the study, various error measures (MPE, MAPE, MAE and RMSE) are used to evaluate the model performance. All of the error measures confirm that forecasted consumption results from ANN are closer to the actual data than forecasted demand from ARIMA. The MPE, MAPE, MAE and RMSE of testing data from ARIMA model are - 2.42%, 4.53%, 9.16 and 12.28, respectively. The MPE, MAPE, MAE and RMSE of testing data from ANN model are - 0.21%, 1.91%, 3.58 and 4.68, respectively. Therefore, ANN is an attractive technique applied for electricity demand forecast in Chiangmai.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Electric power consumptionr
Geographic subdivision Thailand
-- Chiang Mai
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Marpaung, Charles O.P.,
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 Singh, Jai Govind,
Relator term Examination Committee
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element PEA,
Relator term Scholarship donor
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element Asian Institute of Technology 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 Thesis ;
Volume/sequential designation no. ET-11-24
856 ## - ELECTRONIC LOCATION AND ACCESS
Materials specified Full-Text
Uniform Resource Identifier <a href="http://203.159.5.9/ait-thesis/detail.php?q=B02253">http://203.159.5.9/ait-thesis/detail.php?q=B02253</a>
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Cataloger's initials, CIN (RLIN) 130314
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Koha item type 20-AIT Publication
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      Available for Loans Asian Institute of Technology Library Asian Institute of Technology Library AIT Publications 18/08/2026 6.60   AIT Thesis no.ET-11-24 30050120602486 18/08/2026 1 18/08/2026 20-AIT Publication
      Available for Loans Asian Institute of Technology Library Asian Institute of Technology Library AIT Publications 18/08/2026 6.60   AIT Thesis no.ET-11-24 30050120602494 18/08/2026 2 18/08/2026 20-AIT Publication
      Available for Loans Asian Institute of Technology Library Asian Institute of Technology Library Archives 18/08/2026     AIT Thesis no.ET-11-24 30050120368088 18/08/2026   18/08/2026 40-Archives
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