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Full-Text Articles in Engineering
Short-Term Load Forecasting Using Mixed Lazy Learning Method, Seyed-Masoud Barakati, Ali Akbar Gharaveisi, Seyed-Mohammad Reza Rafiei
Short-Term Load Forecasting Using Mixed Lazy Learning Method, Seyed-Masoud Barakati, Ali Akbar Gharaveisi, Seyed-Mohammad Reza Rafiei
Turkish Journal of Electrical Engineering and Computer Sciences
A novel short-term load forecasting method based on the lazy learning (LL) algorithm is proposed. The LL algorithm's input data are electrical load information, daily electricity consumption patterns, and temperatures in a specified region. In order to verify the ability of the proposed method, a load forecasting problem, using the Pennsylvania-New Jersey-Maryland Interconnection electrical load data, is carried out. Three LL models are proposed: constant, linear, and mixed models. First, the performances of the 3 developed models are compared using the root mean square error technique. The best technique is then selected to compete with the state-of-the-art neural network (NN) …
Model-Based Test Case Prioritization Using Cluster Analysis: A Soft-Computing Approach, Ni̇da Gökçe, Fevzi̇ Belli̇, Mübari̇z Emi̇nli̇, Beki̇r Taner Di̇nçer
Model-Based Test Case Prioritization Using Cluster Analysis: A Soft-Computing Approach, Ni̇da Gökçe, Fevzi̇ Belli̇, Mübari̇z Emi̇nli̇, Beki̇r Taner Di̇nçer
Turkish Journal of Electrical Engineering and Computer Sciences
Model-based testing is related to the particular relevant features of the software under test (SUT) and its environment. Real-life systems often require a large number of tests, which cannot exhaustively be run due to time and cost constraints. Thus, it is necessary to prioritize the test cases in accordance with their importance as the tester perceives it, usually given by several attributes of relevant events entailed. Based on event-oriented graph models, this paper proposes an approach to ranking test cases in accordance with their preference degrees. For forming preference groups, events are clustered using an unsupervised neural network and fuzzy …