Recent Development in Electricity Price Forecasting Based on Computational Intelligence Techniques in Deregulated Power Market

The development of artificial intelligence (AI) based techniques for electricity price forecasting (EPF) provides essential information to electricity market participants and managers because of its greater handling capability of complex input and output relationships. Therefore, this research investigates and analyzes the performance of different optimization methods in the training phase of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) for the accuracy enhancement of EPF. In this work, a multi-objective optimization-based feature selection technique with the capability of eliminating non-linear and interacting features is implemented to create an efficient day-ahead price forecasting. In the beginning, the multi-objective binary backtracking search algorithm (MOBBSA)-based feature selection technique is used to examine various combinations of input variables to choose the suitable feature subsets, which minimizes, simultaneously, both the number of features and the estimation error. In the later phase, the selected features are transferred into the machine learning-based techniques to map the input variables to the output in order to forecast the electricity price. Furthermore, to increase the forecasting accuracy, a backtracking search algorithm (BSA) is applied as an efficient evolutionary search algorithm in the learning procedure of the ANFIS approach. The performance of the forecasting methods for the Queensland power market in the year 2018, which is well-known as the most competitive market in the world, is investigated and compared to show the superiority of the proposed methods over other selected methods.© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

The Descent of Preferences

[A slightly revised version of this paper has been accepted by the BJPS] More attention has been devoted to providing evolutionary scenarios accounting for the development of beliefs, or belief-like states, than for desires or preferences. Here I articulate and defend an evolutionary rationale for the development of psychologically real preference states. Preferences token or represent the expected values of discriminated states, available actions, or action-state pairings. The argument is an application the ‘environmental complexity thesis’ found in Godfrey-Smith and Sterelny, although my conclusions differ from Sterelny’s. I argue that tokening expected utilities can, under specified general conditions, be a powerful design solution to the problem of allocating the capacities of an agent in an efficient way. Preferences are for efficient action selection, and are a ‘fuel for success’ in the sense urged by Godfrey-Smith for true beliefs. They will tend to be favoured by selection when environments are complex in ways that matter to an organism, and when organisms have rich behavioural repertoires with heterogenous returns and costs.   The rationale suggested here is conditional, especially on contingencies in what design options are available to selection and on trade-offs associated with the costs of generating and processing representations of value. The unqualified efficiency rationale for preferences suggests that organisms should represent expected utilities in a comprehensive and consistent way, but none of them do. In the final stages of the paper I consider some of the ways in which design trade-offs compromise the implementation of preferences in organisms that have them