LSTM- and GRU-Based Time Series Models for Market Clearing Price Forecasting of Indian Deregulated Electricity Markets

2019 ◽  
pp. 693-700
Author(s):  
Ashish Ubrani ◽  
Simran Motwani
Keyword(s):  
Time Series ◽  
Electricity Markets ◽  
Time Series Models ◽  
Price Forecasting ◽  
Market Clearing ◽  
Market Clearing Price

scholarly journals A Two-Sided Price-Decoupled Pay-As-Bid Auction Approach for the Clearing of Day-Ahead Electricity Markets

2020 ◽  
Vol 162 ◽  
pp. 01006
Author(s):  
Dávid Csercsik
Keyword(s):  
Objective Function ◽  
Electricity Markets ◽  
Supply And Demand ◽  
Conventional Approach ◽  
Small Scale ◽  
Power Balance ◽  
Market Clearing ◽  
Multiunit Auctions ◽  
Market Clearing Price ◽  
Characteristic Features

In this paper we propose a possible alternative for conventional pay-as-clear type multiunit auctions commonly used for the clearing of day-ahead power exchanges, and analyse some of its characteristic features in comparison with conventional clearing. In the proposed framework, instead of the concept of the uniform market clearing price, we introduce limit prices separately for supply and demand bids, and in addition to the power balance constraint, we formulate constraints for the income balance of the market. The total traded quantity is used as the objective function of the formulation. The concept is demonstrated on a simple example and is compared to the conventional approach in small-scale market simulations.

Fuzzy Neural Network Market Clearing Power Price Forecasting Based on K-Means Clustering

2011 ◽  
Vol 121-126 ◽  
pp. 2035-2039 ◽  
Author(s):  
Xian Min Wei
Keyword(s):  
Neural Network ◽  
Prediction Accuracy ◽  
Fuzzy Neural Network ◽  
Fuzzy Inference ◽  
Complex Structure ◽  
Price Forecasting ◽  
Market Clearing ◽  
Market Clearing Price ◽  
Fuzzy Neural ◽  
Long Time

Normalized fuzzy neural network has complex structure, long-time study and other shortcomings. For these shortcomings, this paper applies an improved fuzzy neural network to predict market clearing price. The model is simple, just by k-means clustering to determine the number of fuzzy inference layer nodes, and with strong applicability, higher prediction accuracy.

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