An unsupervised data-driven approach for behind-the-meter photovoltaic power generation disaggregation

2022 ◽  
Vol 309 ◽  
pp. 118450
Author(s):  
Keda Pan ◽  
Zhaohua Chen ◽  
Chun Sing Lai ◽  
Changhong Xie ◽  
Dongxiao Wang ◽  
...  
Keyword(s):  
Power Generation ◽  
Data Driven ◽  
Photovoltaic Power ◽  
Data Driven Approach

A Data-Driven Approach for Estimating the Power Generation of Invisible Solar Sites

2016 ◽  
Vol 7 (5) ◽  
pp. 2466-2476 ◽  
Author(s):  
Hamid Shaker ◽  
Hamidreza Zareipour ◽  
David Wood
Keyword(s):  
Power Generation ◽  
Data Driven ◽  
Data Driven Approach

scholarly journals Fault Diagnosis of Data-Driven Photovoltaic Power Generation System Based on Deep Reinforcement Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shuang Dai ◽  
Dingmei Wang ◽  
Weijun Li ◽  
Qiang Zhou ◽  
Guangke Tian ◽  
...  
Keyword(s):  
Power Generation ◽  
Reinforcement Learning ◽  
Fault Diagnosis ◽  
Data Driven ◽  
Generation System ◽  
State Action ◽  
Photovoltaic Power ◽  
Action Strategy ◽  
Starting Point ◽  
Power Generation System

Aiming at the problem of fault diagnosis of the photovoltaic power generation system, this paper proposes a photovoltaic power generation system fault diagnosis method based on deep reinforcement learning. This method takes data-driven as the starting point. Firstly, the compressed sensing algorithm is used to fill the missing photovoltaic data and then state, action, strategy, and return functions from the environment. Based on the interaction rules and other factors, the fault diagnosis model of the photovoltaic power generation system is established, and the deep neural network is used to approximate the decision network to find the optimal strategy, so as to realize the fault diagnosis of the photovoltaic power generation system. Finally, the effectiveness and accuracy of the method are verified by simulation. The simulation results show that this method can accurately diagnose the fault types of the photovoltaic power generation system, which is of great significance to enhance the security of the photovoltaic power generation system and improve the intelligent operation and maintenance level of the photovoltaic power generation system.

scholarly journals Data-Driven Kernel Extreme Learning Machine Method for the Location and Capacity Planning of Distributed Generation

Energies ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 109 ◽  
Author(s):  
Jingjing Tu ◽  
Yonghai Xu ◽  
Zhongdong Yin
Keyword(s):  
Power Generation ◽  
Distribution System ◽  
Large Scale ◽  
Voltage Stability ◽  
Distribution Network ◽  
Peak Load ◽  
Data Driven ◽  
Support Vector ◽  
Machine Method ◽  
Photovoltaic Power

For the integration of distributed generations such as large-scale wind and photovoltaic power generation, the characteristics of the distribution network are fundamentally changed. The intermittence, variability, and uncertainty of wind and photovoltaic power generation make the adjustment of the network peak load and the smooth control of power become the key issues of the distribution network to accept various types of distributed power. This paper uses data-driven thinking to describe the uncertainty of scenery output, and introduces it into the power flow calculation of distribution network with multi-class DG, improving the processing ability of data, so as to better predict DG output. For the problem of network stability and operational control complexity caused by DG access, using KELM algorithm to simplify the complexity of the model and improve the speed and accuracy. By training and testing the KELM model, various DG configuration schemes that satisfy the minimum network loss and constraints are given, and the voltage stability evaluation index is introduced to evaluate the results. The general recommendation for DG configuration is obtained. That is, DG is more suitable for accessing the lower point of the network voltage or the end of the network. By configuring the appropriate capacity, it can reduce the network loss, improve the network voltage stability, and the quality of the power supply. Finally, the IEEE33&69-bus radial distribution system is used to simulate, and the results are compared with the existing particle swarm optimization (PSO), genetic algorithm (GA), and support vector machine (SVM). The feasibility and effectiveness of the proposed model and method are verified.

Data Driven Approach to Forecast Building Occupant Complaints

2020 ◽  
Author(s):  
Sena Assaf ◽  
Mohamad Awada ◽  
Issam Srour
Keyword(s):  
Data Driven ◽  
Data Driven Approach
Sign in / Sign up

Export Citation Format

Share Document