期刊名称：International Journal of Electrical and Computer Engineering
电子版ISSN：2088-8708
出版年度：2018
卷号：8
期号：3
页码：1324-1330
DOI：10.11591/ijece.v8i3.pp1324-1330
语种：English
出版社：Institute of Advanced Engineering and Science (IAES)
摘要：This paper presents a data acquisition process for solar energy generation and then analyzes the dynamics of its data stream, mainly employing open software solutions such as Python, MySQL, and R. For the sequence of hourly power generations during the period from January 2016 to March 2017, a variety of queries are issued to obtain the number of valid reports as well as the average, maximum, and total amount of electricity generation in 7 solar panels. The query result on all-time, monthly, and daily basis has found that the panel-by-panel difference is not so significant in a university-scale microgrid, the maximum gap being 7.1 % even in the exceptional case. In addition, for the time series of daily energy generations, we develop a neural network-based trace and prediction model. Due to the time lagging effect in forecasting, the average prediction error for the next hours or days reaches 27.6 %. The data stream is still being accumulated and the accuracy will be enhanced by more intensive machine learning.
其他摘要：This paper presents a data acquisition process for solar energy generation and then analyzes the dynamics of its data stream, mainly employing open software solutions such as Python, MySQL, and R. For the sequence of hourly power generations during the period from January 2016 to March 2017, a variety of queries are issued to obtain the number of valid reports as well as the average, maximum, and total amount of electricity generation in 7 solar panels. The query result on all-time, monthly, and daily basis has found that the panel-by-panel difference is not so significant in a university-scale microgrid, the maximum gap being 7.1 % even in the exceptional case. In addition, for the time series of daily energy generations, we develop a neural network-based trace and prediction model. Due to the time lagging effect in forecasting, the average prediction error for the next hours or days reaches 27.6 %. The data stream is still being accumulated and the accuracy will be enhanced by more intensive machine learning.
关键词：Computer and Informatics; Electrical Power;Smart grid; Solar energy; Big data analysis; Stream orchestration; Prediction model
