Zhang Jun Solar Power Generation

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Ultra-short-term solar power forecasting by deep learning and

In this paper, we propose a deep-learning based ultra-short-term solar power prediction with data reconstruction. We decompose the data for the prediction to facilitate extensive exploration of the

Power generation forecasting for solar plants based on Dynamic

In this paper, a novel DBN modeling approach for solar power generation forecasting in solar plants was proposed by fusing multi-source information, including sensor data, operational

Jun Zhang | IEEE Xplore Author Details

Jun Zhang | IEEE Xplore Author Details. Affiliation. Headquarters Product Technology Department, Jingao Solar Energy Technology Co., Ltd, Shanghai, China. Publication Topics.

Jun Zhang

He brings with him extensive research and modeling experience in electricity system analysis, including residential demand, solar PV potential, and renewable integration. Mr. Zhang is highly skilled in

Short-Term Photovoltaic Power Forecasting Based on a Feature Rise

This paper proposes a short-term PV power forecasting method using K-means clustering, ensemble learning (EL), a feature rise-dimensional (FRD) approach, and quantile regression (QR) to

Solar Power Forecasting Using CNN-LSTM Hybrid Model

This research article addresses the imperative need for precise solar power generation forecasting to efficiently integrate solar energy into existing power grids.

Junjun Zhang''s research works | China Electric Power Research

In this paper, it proposes testing technology and research in order to evaluate the power quality for large-scale photovoltaic power station according to latest photovoltaic standards.

Accurate nowcasting of cloud cover at solar photovoltaic

By combining continuous radiance images measured by geostationary satellite and an advanced recurrent neural network, we develop a nowcasting algorithm for predicting cloud fraction

Unsupervised fault detection approach based on depth auto-encoder

Aiming at the problems of frequent failures of photovoltaic power generation system, large amount of operating data and difficult to obtain fault samples, we propose an unsupervised fault

Photovoltaic & Lead-Carbon Batteries

High-efficiency PV batteries and advanced lead-carbon technology with modular racks, integrated BMS, and scalable architecture from 5kWh to 2MWh+. Ideal for solar self-consumption and hybrid microgrids.

Modular Racks & Intelligent EMS

Flexible modular battery racks supporting lead-carbon and lithium chemistries. AI-driven EMS with predictive analytics, real-time load optimization, and seamless solar inverter integration.

Industrial & Telecom Cabinets

Rugged industrial battery cabinets and IP55-rated telecom outdoor enclosures for base stations, data centers, and commercial complexes. Integrated thermal management and remote monitoring.

Commercial Storage & Microgrids

Turnkey solutions for shopping centers, office complexes, and remote microgrids. Combines PV arrays, battery banks, intelligent EMS, and grid/diesel integration for energy independence.

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