Optimal combined load forecast based on multi-criteria decision making methods

• Main Author: Radhi Kamaruzaman, Ahmad Khairul
• Format: Thesis
• Language: English; English; English
• Published: 2013
• Subjects: TK1001-1841 Production of electric energy or power. Powerplants. Central stations
• Online Access: http://eprints.uthm.edu.my/6623/1/24p%20AHMAD%20KHAIRUL%20RADHI%20KAMARUZAMAN.pdf; http://eprints.uthm.edu.my/6623/2/AHMAD%20KHAIRUL%20RADHI%20KAMARUZAMAN%20COPYRIGHT%20DECLARATION.pdf; http://eprints.uthm.edu.my/6623/3/AHMAD%20KHAIRUL%20RADHI%20KAMARUZAMAN%20WATERMARK.pdf; http://eprints.uthm.edu.my/6623/
• Institution: Universiti Tun Hussein Onn Malaysia

Owing to the importance of load forecasting, accurate models for electric power load forecasting are essential to the operation and planning of a utility company. Their main idea is to establish the mathematical optima model for forecasting, intend to match the data, and make predict error least, and attain superior forecast result. This paper present the analyzing of soft method such as decision making analyses to solve load forecast in power system demand that are unstructured problems of multi-factors. The combined forecasting problem is treated as multihierarchies and multi-factors evaluation by composing qualitative analyses and quantitative calculation. In addition, the experiences and judgments of experts will be collected to implement judgment matrices in group decision making. This paper proposed the soft method based on Analytic Hierarchy Process (AHP), Fuzzy Analytic Hierarchy Process (Fuzzy AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to carry out long middle term load demand combined forecast. A hierarchy structure has been established by analyzing various factors that affect the load forecast. It is the key to determine the combined weight coefficients in the optimal combined forecasting method. Fuzzy complementary judgment matrixes of pair-wise comparison will be formed by expert in each hierarchy and be converted to a fuzzy consistent matrix. The eigenvector can be calculated using its general formula and be regarded as weight coefficient in combined forecasting. The combined forecast methods based on the Analytic Hierarchy Process (AHP), Fuzzy Analytic Hierarchy Process (Fuzzy AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are of clear hierarchy structure, sufficient judgment information and simple calculation formula. The forecasting examples show that this method is practical, convenient and accurate.