6,640 research outputs found
Development of Neurofuzzy Architectures for Electricity Price Forecasting
In 20th century, many countries have liberalized their electricity market. This power markets liberalization has directed generation companies as well as wholesale buyers to undertake a greater intense risk exposure compared to the old centralized framework. In this framework, electricity price prediction has become crucial for any market player in their decisionâmaking process as well as strategic planning. In this study, a prototype asymmetricâbased neuroâfuzzy network (AGFINN) architecture has been implemented for shortâterm electricity prices forecasting for ISO New England market. AGFINN framework has been designed through two different defuzzification schemes. Fuzzy clustering has been explored as an initial step for defining the fuzzy rules while an asymmetric Gaussian membership function has been utilized in the fuzzification part of the model. Results related to the minimum and maximum electricity prices for ISO New England, emphasize the superiority of the proposed model over wellâestablished learningâbased models
Encoding Seasonal Climate Predictions for Demand Forecasting with Modular Neural Network
Current time-series forecasting problems use short-term weather attributes as
exogenous inputs. However, in specific time-series forecasting solutions (e.g.,
demand prediction in the supply chain), seasonal climate predictions are
crucial to improve its resilience. Representing mid to long-term seasonal
climate forecasts is challenging as seasonal climate predictions are uncertain,
and encoding spatio-temporal relationship of climate forecasts with demand is
complex.
We propose a novel modeling framework that efficiently encodes seasonal
climate predictions to provide robust and reliable time-series forecasting for
supply chain functions. The encoding framework enables effective learning of
latent representations -- be it uncertain seasonal climate prediction or other
time-series data (e.g., buyer patterns) -- via a modular neural network
architecture. Our extensive experiments indicate that learning such
representations to model seasonal climate forecast results in an error
reduction of approximately 13\% to 17\% across multiple real-world data sets
compared to existing demand forecasting methods.Comment: 15 page
Electricity Price Forecasting using Asymmetric Fuzzy Neural Network Systems
Electricity price forecasting is considered as an important tool for energy-related utilities and power generation industries. The deregulation of power market, as well as the competitive financial environment, which have introduced new market players in this field, makes the electricity price forecasting problem a demanding mission. The main focus of this paper is to investigate the performance of asymmetric neuro-fuzzy network models for day-ahead electricity price forecasting. The proposed model has been developed from existing TakagiâSugenoâKang fuzzy systems by substituting the IF part of fuzzy rules with an asymmetric Gaussian function. In addition, a clustering method is utilised as a pre-processing scheme to identify the initial set and adequate number of clusters and eventually the number of rules in the proposed model. The results corresponding to the minimum and maximum electricity price have indicated that the proposed forecasting scheme could be considered as an improved tool for the forecasting accuracy
Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors
Real-world time series is characterized by intrinsic non-stationarity that
poses a principal challenge for deep forecasting models. While previous models
suffer from complicated series variations induced by changing temporal
distribution, we tackle non-stationary time series with modern Koopman theory
that fundamentally considers the underlying time-variant dynamics. Inspired by
Koopman theory of portraying complex dynamical systems, we disentangle
time-variant and time-invariant components from intricate non-stationary series
by Fourier Filter and design Koopman Predictor to advance respective dynamics
forward. Technically, we propose Koopa as a novel Koopman forecaster composed
of stackable blocks that learn hierarchical dynamics. Koopa seeks measurement
functions for Koopman embedding and utilizes Koopman operators as linear
portraits of implicit transition. To cope with time-variant dynamics that
exhibits strong locality, Koopa calculates context-aware operators in the
temporal neighborhood and is able to utilize incoming ground truth to scale up
forecast horizon. Besides, by integrating Koopman Predictors into deep residual
structure, we ravel out the binding reconstruction loss in previous Koopman
forecasters and achieve end-to-end forecasting objective optimization. Compared
with the state-of-the-art model, Koopa achieves competitive performance while
saving 77.3% training time and 76.0% memory
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