A hybrid model for day-ahead electricity price forecasting: Combining fundamental and stochastic modelling

The accurate prediction of short-term electricity prices is vital for effective trading strategies, power plant scheduling, profit maximisation and efficient system operation. However, uncertainties in supply and demand make such predictions challenging. We propose a hybrid model that combines a techno-economic energy system model with stochastic models to address this challenge. The techno-economic model in our hybrid approach provides a deep understanding of the market. It captures the underlying factors and their impacts on electricity prices, which is impossible with statistical models alone. The statistical models incorporate non-techno-economic aspects, such as the expectations and speculative behaviour of market participants, through the interpretation of prices. The hybrid model generates both conventional point predictions and probabilistic forecasts, providing a comprehensive understanding of the market landscape. Probabilistic forecasts are particularly valuable because they account for market uncertainty, facilitating informed decision-making and risk management. Our model delivers state-of-the-art results, helping market participants to make informed decisions and operate their systems more efficiently

Acquisition of stock trading strategy using predicted stock price

In recent years, stock price prediction and stock trading using machine learning have become useful. In stock trading using machine learning, the present-day stock price is commonly used. However, making a profit from reinforcement learning using this stock price is difficult. Therefore, the aim of this research is to make a profit by performing reinforcement learning using the predicted stock price. The results show that using the predicted stock price can create profits in the Japanese market

Comparison of Performance Analysis using Different Neural Network and Fuzzy Logic Models for Prediction of Stock Price

Analysis and prediction of stock market is very interesting as this helps the financial experts in decision making and in turn profit making. In this thesis simple feed forward neural network (FFNN) model is initially considered for stock market prediction and its result is compared with Radial basis function network (RBFN) model, fuzzy logic model and Elman network model. A FFNN model can fit into any finite input-output mapping problem where the FFNN consists of one hidden layer and enough neurons in the hidden layer. RBFN are the Artificial Neural Networks (ANN) in which Radial Basis Functions (RBF) are used as activation functions. In this thesis, Levenberg-Marquardt Backpropagation algorithm is used to train the data for both FFNN and Elman network. For Fuzzy Logic, Sugeno type Fuzzy Inference System (FIS) is used to model the prediction process. Different Clustering methods are used to nd the optimal parameters of RBF. These techniques were tested with published stock market data of National Stock Exchange of India Ltd. for validation

Oligopoly game: Price makers meet price takers

© 2018 Elsevier B.V. The paper studies an oligopoly game, where firms can choose between price-taking and price-making strategies. On a mixed market price takers are always better off than price makers, though the profits of both types decline in the number of price takers. We investigate and confront two possibilities of firms’ decisions about their types: forward-looking equilibrium reasoning and backward-looking individual learning. We find that the Cournot outcome is the only equilibrium prediction and it is learnable if firms are sufficiently sensitive to profit differences. However, with a larger number of firms, a unilateral deviation from Cournot behavior becomes profitable. Under learning this incentive creates a space for permanent oscillations over different markets with a positive but low number of price takers

The Curse of Knowledge in Economic Settings: An Experimental Analysis

In economic analyses of asymmetric information, better-informed agents are assumed capable of reproducing the judgments of less-informed agents. We discuss a systematic violation of this assumption that we call the "curse of knowledge." Better-informed agents are unable to ignore private information even when it is in their interest to do so; more information is not always better. Comparing judgments made in individual-level and market experiments, we find that market forces reduce the curse by approximately 50 percent but do not eliminate it. Implications for bargaining, strategic behavior by firms, principal-agent problems, and choice under un-certainty are discussed

Strategic participation in competitive electricity markets: Internal versus sectorial data analysis

[EN] Current approaches for risk management in energy market participation mostly refer to portfolio optimization for long-term planning, and stochastic approaches to deal with uncertainties related to renewable energy gen- eration and market prices variation. Risk assessment and management as integrated part of actual market ne- gotiation strategies is lacking from the current literature. This paper addresses this gap by proposing a novel model for decision support of players’ strategic participation in electricity market negotiations, which considers risk management as a core component of the decision-making process. The proposed approach addresses the adaptation of players’ behaviour according to the participation risk, by combining the two most commonly used approaches of forecasting in a company’s scope: the internal data analysis, and the external, or sectorial, data analysis. The internal data analysis considers the evaluation of the company’s evolution in terms of market power and profitability, while the sectorial analysis addresses the assessment of the competing entities in the market sector using a K-Means-based clustering approach. By balancing these two components, the proposed model enables a dynamic adaptation to the market context, using as reference the expected prices from com- petitor players, and the market price prediction by means of Artificial Neural Networks (ANN). Results under realistic electricity market simulations using real data from the Iberian electricity market operator show that the proposed approach is able to outperform most state-of-the-art market participation strategies, reaching a higher accumulated profit, by adapting players’ actions according to the participation risk