Electricity, Heat, and Gas Load Forecasting Based on Deep Multitask Learning in Industrial-Park Integrated Energy System

• Published in: Entropy (Basel, Switzerland) Vol. 22; no. 12; p. 1355
• Main Authors: Zhang, Linjuan, Shi, Jiaqi, Wang, Lili, Xu, Changqing
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 30.11.2020; MDPI
• Subjects: Algorithms; Alternative energy sources; Artificial intelligence; Belief networks; Consumption; Deep learning; Economic forecasting; Electrical loads; Electricity; Electricity consumption; Electricity distribution; energy forecasting for electricity; Forecasting; heat and gas; industrial-park; integrated energy system; Integrated energy systems; Learning; Machine learning; Mathematical models; multitask; Natural gas; Neural networks; Voice recognition; deep learning; multitask; heat and gas; energy forecasting for electricity; integrated energy system; industrial-park

Different energy systems are closely connected with each other in industrial-park integrated energy system (IES). The energy demand forecasting has important impact on IES dispatching and planning. This paper proposes an approach of short-term energy forecasting for electricity, heat, and gas by emp...