การทำนายปริมาณการใช้ไฟฟ้าเฉลี่ยต่อคนโดยใช้การเรียนรู้ของเครื่องแบบแปลผลได้ ข้อมูลจากประเทศไทยในช่วงปี 1973 จนถึง 2021
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ความต้องการใช้ไฟฟ้าที่เพิ่มขึ้นอย่างต่อเนื่องก่อให้เกิดปัญหาสำคัญต่อผู้กำหนดนโยบายและบริษัทสาธารณูปโภค การพยากรณ์การใช้ไฟฟ้าที่แม่นยำมีความสำคัญอย่างยิ่งต่อการจัดการพลังงานอย่างมีประสิทธิภาพ วิทยานิพนธ์ฉบับนี้นำเสนอแนวทางการเรียนรู้ของเครื่องที่สามารถอธิบายได้ (Interpretable Machine Learning) สำหรับการพยากรณ์ความต้องการพลังงาน โดยผสานปัจจัยเศรษฐกิจมหภาค เช่น ผลิตภัณฑ์มวลรวมภายในประเทศ (GDP) อัตราเงินเฟ้อ และการเติบโตของภาคอุตสาหกรรม การศึกษานี้ใช้ข้อมูลจากฐานข้อมูล World Development Indicators ในช่วงปี พ.ศ. 2516 ถึง พ.ศ. 2564 และใช้แบบจำลอง Multilayer Perceptron ผลการวิจัยเน้นย้ำถึงความสำคัญของการพิจารณาปัจจัยทางเศรษฐกิจต่อความต้องการใช้ไฟฟ้า ซึ่งนำไปสู่แนวทางที่เชื่อถือได้สำหรับการพยากรณ์การใช้พลังงานไฟฟ้า การศึกษานี้ช่วยเพิ่มพูนองค์ความรู้โดยการนำเสนอแบบจำลองที่ชัดเจนซึ่งสนับสนุนกระบวนการตัดสินใจที่เกี่ยวข้องกับการจัดการพลังงานและการกำหนดนโยบาย
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