TY - GEN
T1 - Big Data Analytics for Electricity Price Forecast
AU - Yousefi, Ashkan
AU - Ameri Sianaki, Omid
AU - Jan, Tony
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - Electricity Price forecast is a major task in smart grid operation. There is a massive amount of data flowing in the power system including the data collection by control systems, sensors, etc. In addition, there are many data points which are not captured and processed by the energy market operators and electricity network operators including gross domestic product, the price of fuel, government policy and incentives for renewable and green energy sectors as well as impacts on new technologies such as battery technology advancement and electric vehicles. In this study, data points from 2001 to 2017 were collected and 78 data points are considered for analyses to select the highly-correlated features which could potentially affect the electricity price. In the first step, a comprehensive correlation method using Pearson Correlation Coefficient is applied to find the points which are correlated with the electricity price. In the next step, the correlated data is fed to the machine learning algorithm for price forecast. The algorithm results were tested in the historical data in California and the outcomes were satisfactory for the three years forecast. The combination of featured selection and machine learning is giving superior outcomes than the traditional methods.
AB - Electricity Price forecast is a major task in smart grid operation. There is a massive amount of data flowing in the power system including the data collection by control systems, sensors, etc. In addition, there are many data points which are not captured and processed by the energy market operators and electricity network operators including gross domestic product, the price of fuel, government policy and incentives for renewable and green energy sectors as well as impacts on new technologies such as battery technology advancement and electric vehicles. In this study, data points from 2001 to 2017 were collected and 78 data points are considered for analyses to select the highly-correlated features which could potentially affect the electricity price. In the first step, a comprehensive correlation method using Pearson Correlation Coefficient is applied to find the points which are correlated with the electricity price. In the next step, the correlated data is fed to the machine learning algorithm for price forecast. The algorithm results were tested in the historical data in California and the outcomes were satisfactory for the three years forecast. The combination of featured selection and machine learning is giving superior outcomes than the traditional methods.
UR - http://www.scopus.com/inward/record.url?scp=85064880010&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-15035-8_90
DO - 10.1007/978-3-030-15035-8_90
M3 - Conference contribution
AN - SCOPUS:85064880010
SN - 9783030150341
T3 - Advances in Intelligent Systems and Computing
SP - 915
EP - 922
BT - Web, Artificial Intelligence and Network Applications - Proceedings of the Workshops of the 33rd International Conference on Advanced Information Networking and Applications WAINA-2019
A2 - Xhafa, Fatos
A2 - Takizawa, Makoto
A2 - Barolli, Leonard
A2 - Enokido, Tomoya
PB - Springer Verlag
T2 - 33rd International Conference on Advanced Information Networking and Applications, AINA 2019
Y2 - 27 March 2019 through 29 March 2019
ER -
