Author
Sílvio Mariano
Bio: Sílvio Mariano is an academic researcher from University of Beira Interior. The author has contributed to research in topics: Switched reluctance motor & Photovoltaic system. The author has an hindex of 19, co-authored 112 publications receiving 1715 citations.
Papers published on a yearly basis
Papers
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TL;DR: In this article, a neural network approach for forecasting short-term electricity prices is proposed. But the authors focus on the short term and do not consider the long-term forecast of electricity prices, and use a three-layered feed-forward neural network for forecasting next-week electricity prices.
402 citations
TL;DR: Comparisons with other published methods demonstrate that the proposed GCPSO method produces very good results in the extraction of the PV model parameters, which can find highly accurate solutions while demanding a reduced computational cost.
174 citations
TL;DR: In this paper, the authors proposed a nonlinear approach to solve the short-term hydro scheduling problem under deregulation, considering head-dependency, in a case study based on one of the main Portuguese cascaded hydro systems.
Abstract: In this paper, we propose a novel nonlinear approach to solve the short-term hydro scheduling problem under deregulation, considering head-dependency. The actual size of hydro systems, the continuous reservoir dynamics and constraints, the hydraulic coupling of cascaded hydro systems, and the complexity associated with head-sensitive hydroelectric power generation still pose a real challenge to the modelers. These concerns are all accounted for in our approach. Results from a case study based on one of the main Portuguese cascaded hydro systems are presented, showing that the proposed nonlinear approach is proficient.
163 citations
TL;DR: A hybrid method that combines similar and recent day-based selection, correlation and wavelet analysis in a pre-processing stage is presented, revealing positive forecasting results in comparison with other state-of-the-art methods.
94 citations
TL;DR: A new hybrid methodology that combines diversification and intensification mechanisms from different metaheuristics (MHs) to estimate PV parameters precisely has the capacity to adapt to the specific optimization problem and maintain diversity when building solutions, thus mitigating premature convergence and population stagnation.
78 citations
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TL;DR: In this paper, a review article aims to explain the complexity of available solutions, their strengths and weaknesses, and the opportunities and threats that the forecasting tools offer or that may be encountered.
1,016 citations
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TL;DR: In this article, a review article aims at explaining the complexity of available solutions, their strengths and weaknesses, and the opportunities and treats that the forecasting tools offer or that may be encountered.
Abstract: A variety of methods and ideas have been tried for electricity price forecasting (EPF) over the last 15 years, with varying degrees of success. This review article aims at explaining the complexity of available solutions, their strengths and weaknesses, and the opportunities and treats that the forecasting tools offer or that may be encountered. The paper also looks ahead and speculates on the directions EPF will or should take in the next decade or so. In particular, it postulates the need for objective comparative EPF studies involving (i) the same datasets, (ii) the same robust error evaluation procedures and (iii) statistical testing of the significance of the outperformance of one model by another.
1,007 citations
TL;DR: In this article, the authors provide a comprehensive and systematic literature review of Artificial Intelligence based short-term load forecasting techniques and provide the major objective of this study is to review, identify, evaluate and analyze the performance of artificial Intelligence based load forecast models and research gaps.
Abstract: Electrical load forecasting plays a vital role in order to achieve the concept of next generation power system such as smart grid, efficient energy management and better power system planning. As a result, high forecast accuracy is required for multiple time horizons that are associated with regulation, dispatching, scheduling and unit commitment of power grid. Artificial Intelligence (AI) based techniques are being developed and deployed worldwide in on Varity of applications, because of its superior capability to handle the complex input and output relationship. This paper provides the comprehensive and systematic literature review of Artificial Intelligence based short term load forecasting techniques. The major objective of this study is to review, identify, evaluate and analyze the performance of Artificial Intelligence (AI) based load forecast models and research gaps. The accuracy of ANN based forecast model is found to be dependent on number of parameters such as forecast model architecture, input combination, activation functions and training algorithm of the network and other exogenous variables affecting on forecast model inputs. Published literature presented in this paper show the potential of AI techniques for effective load forecasting in order to achieve the concept of smart grid and buildings.
673 citations
TL;DR: A novel modeling framework for forecasting electricity prices is proposed and it is shown how the proposed deep learning models outperform the state-of-the-art methods and obtain results that are statistically significant.
406 citations