This paper proposes that artificial intelligence techniques and non-intrusive energy-managing technology (NIEM) will effectively manage energy demands within economic dispatch strategy analysis for the cogeneration plant and power utility. To test the performance of the proposed approach, data sets for electrical loads in the factories were analyzed and established using an electromagnetic transient program (EMTP) and onsite load measurement. The artificial intelligence techniques were applied to data extraction and factory load identification, especially for nonintrusive energy management. The effectiveness of factory load identification was analyzed and compared using different classifier methods. The strategy analysis revealed that the analysis of economic dispatch strategy for the cogeneration plant and power utility in the way of energy demands using the NIEM can estimate reasonably energy contribution from the cogeneration plant and/or power utility, and further improve air pollution. The application of artificial intelligence can reduce greatly the computation time and the size of memory for factory load identification and energy calculation.

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Transparent conductive oxides of Sn-doped ZnO (SZO) films with doping weight ratios of 2.0, 3.0, 4.0, and 5.0 wt % have been deposited on indium tin oxide (ITO)/poly(ethylene terephthalate) (PET) and PET flexible substrates at room temperature by pulsed laser deposition (PLD). Resultant films of SZO on ITO/PET and PET flexible substrates are amorphous in phase. It is found that undoped and SZO films on ITO/PET is anomalously better than films on PET in optical transmittance in the range of longer wavelength, possibly due to the refraction index difference between SZO, ITO films, and PET substrates, Burstein–Moss effect and optical interference of SZO/ITO bilayer films and substrate materials, and furthermore resulting in the decrement of reflection. The lowest electrical resistivity (ρ) of 4.0 wt % SZO films on flexible substrates of PET and ITO/PET are 3.8×10-2 and ρ= 1.2×10-2 Ω·cm, respectively. It is found that electrical and optical properties of the resultant films are greatly dependent on various amount of Sn element doping effect and substrate material characteristics.

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As now, an e-Business Platform has been spread all over the world by using Internet to sale products. A heuristic Questionnaire Collect System (QCS) will be introduced in this paper. The QCS includes a Questionnaire Analysis Machine (QAM) and a Questionnaire Collect Sub-System (QCSS). QCSS, which is assembled by Questionnaire Echo Machine (QEM), QuestionnaireCollect Machine (QCM), and Questionnaire Collect Sub-Machine (QCSM), is built in different supermarket or department store. The QEM is usually put inside the exhibition area. It will show and speak to buyers the product information and some questionnaires from the LCD monitor and from speaker respectively, when the buyer presses its key pad. QEM will send an echo signal to the QCM via radio frequency (RF) transceiver after buyers give an echo by pressing the key pad of the QEM. It will be easy, quick, and automatic data mining technology.