Navegando por Autor "Cohen, Miri Weiss"

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    Forecasting in non-stationary environments with fuzzy time series.
    (2020) Silva, Petrônio Cândido de Lima e; Severiano Junior, Carlos Alberto; Alves, Marcos Antonio; Silva, Rodrigo; Cohen, Miri Weiss; Guimarães, Frederico Gadelha
    Time series arise in many fields of science such as engineering, economy and agriculture to cite a few. In the early 1990’s the so called Fuzzy Time Series were proposed to handle vague and imprecise knowledge in time series data and have since become competitive forecasting models. A common limitation of recent fuzzy time series models is their inability to handle non-stationary data. Thus, in this paper we introduce a Non-Stationary Fuzzy Time Series (NSFTS). In the proposed method, we employ Non-Stationary Fuzzy Sets, in which perturbation functions are used to adapt the membership function parameters in the knowledge base in response to statistical changes in the time series. The flexibility of the method by means of computational experiments was tested with eight synthetic non-stationary time series data with several kinds of concept drifts, four real market indices (Dow Jones, NASDAQ, SP500 and TAIEX), three real FOREX pairs (EUR-USD, EUR-GBP, GBP-USD), and two real cryptocoins exchange rates (Bitcoin-USD and Ethereum-USD). As competitor models the Time Variant fuzzy time series and the Incremental Ensemble were used, these are two of the major approaches for handling non-stationary data sets. The proposed method shows resilience to concept drift, by adapting parameters of the model, while preserving the symbolic structure of the knowledge base.
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    Multi-objective energy storage power dispatching using plug-in vehicles in a smart-microgrid.
    (2016) Coelho, Vitor Nazário; Coelho, Igor Machado; Coelho, Bruno Nazário; Cohen, Miri Weiss; Reis, Agnaldo José da Rocha; Silva, Sidelmo Magalhães; Souza, Marcone Jamilson Freitas; Fleming, Peter J.; Guimarães, Frederico Gadelha
    This paper describes a multi-objective power dispatching problem that uses Plug-in Electric Vehicle (PEV) as storage units.We formulate the energy storage planning as a Mixed-Integer Linear Programming (MILP) problem, respecting PEV requirements, minimizing three different objectives and analyzing three different criteria. Two novel cost-to-variability indicators, based on Sharpe Ratio, are introduced for analyzing the volatility of the energy storage schedules. By adding these additional criteria, energy storage planning is optimized seeking to minimize the following: total Microgrid (MG) costs; PEVs batteries usage; maximum peak load; difference between extreme scenarios and two Sharpe Ratio indices. Different scenarios are considered, which are generated with the use of probabilistic forecasting, since prediction involves inherent uncertainty. Energy storage planning scenarios are scheduled according to information provided by lower and upper bounds extracted from probabilistic forecasts. A MicroGrid (MG) scenario composed of two renewable energy resources, a wind energy turbine and photovoltaic cells, a residential MG user and different PEVs is analyzed. Candidate non-dominated solutions are searched from the pool of feasible solutions obtained during different Branch and Bound optimizations. Pareto fronts are discussed and analyzed for different energy storage scenarios. Perhaps the most important conclusion from this study is that schedules that minimize the total system cost may increase maximum peak load and its volatility over different possible scenarios, therefore may be less robust.