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Title: Short Term Load Forecasting for Smart Power Systems
Researcher: Jain, Babita Kumari
Guide(s): Jain, Amit
Keywords: Engineering and Technology,Engineering,Engineering Electrical and Electronic
Short Term Load Forecasting, Euclidean Distance, Fuzzy Logic, Particle Swarm Optimization, Evolutionary Particle Swarm Optimization, New Particle Swarm Optimization, Clustering, Regression, Least Square Regression, Mean Absolute percentage Error
University: International Institute of Information Technology, Hyderabad
Completed Date: 24/11/2018
Abstract: The work presents Artificial Intelligence (AI) and Data Mining based formulation for STLF combined with Statistical Techniques. The Temperature, Humidity and Day Type are considered as they are significant factors impacting the effectiveness of an accurate STLF. A Euclidean Norm based Similar Day Approach using the Correction Factors generated by the Fuzzy Inference System has been developed initially for the STLF and novelty is introduced in this methodology by assigning weights to various variables used in the Euclidean Norm. Further to this the input parameter limits of the FIS have been optimized using three Swarm Intelligence Techniques namely Particle Swarm Optimization (PSO), New Particle Swarm Optimization (NPSO) and Evolutionary Particle Swarm Optimization (EPSO). The models have been used to perform the STLF on a 7 months dataset as well as data of 3 years as historic dataset. The results of all the three techniques were found to be good. newlineAnother novel methodology, which amalgamates the Clustering Technique of Data Mining with the Regression Technique, has been developed in this thesis to give a more accurate STLF. The zest of this technique is that clustering brings together the very similar days of the forecast day in one cluster and regression technique further encapsulates the total correlation of load and weather variables of the similar days in the cluster, hence enhancing the forecast efficiency. newlineThe research presented in this thesis also deals with the very important issue of STLF for special days. This research presents a Hybrid Data Mining based formulation of STLF with emphasis on special days and anomalous days, such as public and national holidays, which are often ignored during the general modelling process. The results for the normal and special days have been quite encouraging with the Mean Absolute Percentage Error (MAPE) for most of the special days coming out to be less than 3.0%. newline
Pagination: All Pages
Appears in Departments:IT in Power Systems

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01_title.pdfAttached File484.15 kBAdobe PDFView/Open
02_certificates.pdf367.31 kBAdobe PDFView/Open
03_acknowledgements.pdf344.07 kBAdobe PDFView/Open
04_contents.pdf436.27 kBAdobe PDFView/Open
05_list of tables figures.pdf391.92 kBAdobe PDFView/Open
06_nomenclature.pdf470.88 kBAdobe PDFView/Open
07_chapter 1.pdf749.76 kBAdobe PDFView/Open
08_chapter 2.pdf727.41 kBAdobe PDFView/Open
09_chapter 3.pdf1.11 MBAdobe PDFView/Open
10_chapter 4.pdf1.76 MBAdobe PDFView/Open
11_chapter 5.pdf1.01 MBAdobe PDFView/Open
12_chapter 6.pdf873.3 kBAdobe PDFView/Open
13_chapter 7.pdf683.61 kBAdobe PDFView/Open
14_chapter 8.pdf1.87 MBAdobe PDFView/Open
15_chapter 9.pdf550.48 kBAdobe PDFView/Open
16_references.pdf521.78 kBAdobe PDFView/Open

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