Stock market’s volatile and complex nature makes it difficult to predict the market situation. Deep Learning is capable of simulating analyzing patterns in unstructured data. learning models have applications image recognition, speech natural language processing (NLP), many more. Its application stock prediction gaining attention because its capacity handle large datasets data mapping with accu...

In this paper, we survey 82 papers related to revenue management and dynamic pricing of electricity and lists future research avenues in this field. Dynamic pricing has the potential to modify electric load profiles by charging different prices at different demand levels and hence can act as an effective demand side management tool. There are different forms of dynamic prices that can be offere...

This paper presents an artificial neural network, ANN, based approach for estimating short-term wholesale electricity prices using past price and demand data. The objective is to utilize the piecewise continuous nature of electricity prices on the time domain by clustering the input data into time ranges where the variation trends are maintained. Due to the imprecise nature of cluster boundarie...

This project is focused on the design of price forecasting tools for market operators and for market traders, taking into account the distinct purposes, data availability, and time horizons of the distinct users. Empirical price data from the MISO and RTE have been analyzed. A combined model is developed to forecast MISO day-ahead nodal prices. An ARIMA model is constructed to forecast RTE week...