NDLI: Probabilistic deep neural network price forecasting based on residential load and wind speed predictions

Content Provider	IET Digital Library
Author	Afrasiabi, Mousa Mohammadi, Mohammad Rastegar, Mohammad Kargarian, Amin
Abstract	Precise price forecasting can lessen the risk of participation in the deregulated electricity market. On account of a large amount of historical data, deep learning based methods can be a promising solution to achieve an accurate forecast. This study presents a deep neural network algorithm to estimate the probability density function of price, incorporating the prediction of wind speed and residential load as two other high volatile parameters. To this end, first, a combination of convolution neural network (CNN) and gated recurrent unit (GRU) is utilised to predict wind speed and residential load. Then, the results are integrated into historical price information to form the input dataset for price forecasting. The proposed price forecast procedure consists of CNN, GRU, and adaptive kernel density estimator (AKDE). AKDE is used as a numerical algorithm to capture probabilistic characterisation of real-time and day-ahead prices. Several deep and shallow networks and the proposed algorithm are implemented, and the results are compared. Furthermore, the effectiveness of AKDE in providing complete statistical information is verified through comparison with conventional and fixed smooth KDEs. In addition, the gradient boosting tree method is incorporated to verify the dependence of the price to the wind and the residential loads.
Starting Page	1840
Ending Page	1848
Page Count	9
ISSN	17521416
Volume Number	13
e-ISSN	17521424
Issue Number	Issue 11, Aug (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rpg/13/11
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rpg.2018.6257
Journal	IET Renewable Power Generation
Publisher Date	2019-04-29
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Accurate Forecast Adaptive Kernel Density Estimator AKDE CNN Convolution Neural Network Day-ahead Prices Deep Learning Based Method Deep Network Deep Neural Network Algorithm Deregulated Electricity Market Forecasting Theory GRU High Volatile Parameter Historical Data Historical Price Information Knowledge Engineering Technique Learning in AI Load Forecasting Neural Computing Technique Neural Nets Power Engineering Computing Power Market Power System Economics Power System Managemen Power System Operation Power System Planning And Layout Precise Price Forecasting Price Forecast Procedure Pricing Probabilistic Deep Neural Network Price Forecasting Probability Probability Density Function Residential Load Residential Load Prediction Shallow Network Statistics Wind Speed Predictions
Content Type	Text
Resource Type	Article
Subject	Renewable Energy, Sustainability and the Environment

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