Electricity demand management is most crucial part for Nepal Electricity Authority (NEA) in case of Nepal. Electricity demand is mainly affected by time of day, week of day, monthly season and ambient temperature of power substation customer, their population trained and their life style according to time period. Mostly in winter most of electricity consumer use heating appliances and in summer cooling appliances. Also, in daily pattern electricity demand is high during morning and evening time due to peoples used cooking, lighting and entertainment appliances. That varies the load on power grid. By leveraging historical electricity demand data, time and meteorological records, we have to identify correlations and seasonal and time series patterns using statistical and machine learning techniques and predict short time electricity demand on power substation (hourly). In this system we use multivariable LSTM based-short term electricity demand forecasting, which can predict with 99% maximum accuracy.
목차
Abstract 1. Introduction 2. Material and method 2.1. Study area 2.2 Sample data 3. Methodology 3.1 Data collection 3.2 Data analysis 3.3 Model Development/Pipeline 4. Conclusion Acknowledgments References
키워드
LSTMMultivariableTime seriesShort term electricity demandhistorical electricity demandtimemetrological records.
저자
Anil Gharti [ Department Artificial Intelligence, Kathmandu University ]
Yagya Raj Pandeya [ Department Artificial Intelligence, Kathmandu University ]
Corresponding Author
한국AI디지털융합학회(구 한국디지털융합학회) [The Korean Academic Society of AI Digital Convergence]
설립연도
2015
분야
사회과학>경영학
소개
본 학회는 디지털 경영에 관련된 디지털 미디어, 디지털 통신, 디지털 방송, 디지털 콘텐츠, 디지털 문화, 디지털 사회, 디지털 유통, 디지털 금융, 디지털 물류, 디지털 정책, 디지털 기술, 디지털 교육 그리고 디지털과 아날로그의 비교 등에 대한 학제간 연구와 실사구시적인 적용을 통하여 디지털 경영의 발전과 한국이 세계적인 디지털 강국으로 성장하기 위한 학술적인 기반과 실무적인 지침을 조성하는 것을 목적으로 하고 있습니다.
간행물
간행물명
IJICTDC [International Journal of Information Communication Technology and Digital Convergence]