Qinghe Zhao

Sep 24, 2024 | preprints.org | Qinghe Zhao |Yue Hao |Xuechen Li

Preprint Article Version 1 This version is not peer-reviewed Version 1 : Received: 24 September 2024 / Approved: 24 September 2024 / Online: 25 September 2024 (04:29:40 CEST) Zhao, Q.; Hao, Y.; Li, X. Stock Price Prediction Based on Hybrid CNN-LSTM Model. Preprints 2024, 2024091904. https://doi.org/10.20944/preprints202409.1904.v1 Zhao, Q.; Hao, Y.; Li, X. Stock Price Prediction Based on Hybrid CNN-LSTM Model. Preprints 2024, 2024091904.

Dec 8, 2023 | mdpi.com | Qinghe Zhao |Xinyi Liu |Junlong Fang

1. IntroductionLoad forecasting, also called power energy demand forecasting, is a crucial business in need of both commerce and engineering. Forecasting provides a guarantee of stability and a decision reference for power supply planning, transmission, and distribution systems planning, as well as power systems operations and maintenance, financial planning, and rate design.