31,152 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 decisionāmaking process as well as strategic planning. In this study, a prototype asymmetricābased neuroāfuzzy network (AGFINN) architecture has been implemented for shortāterm 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 wellāestablished learningābased models
Multi-task additive models with shared transfer functions based on dictionary learning
Additive models form a widely popular class of regression models which
represent the relation between covariates and response variables as the sum of
low-dimensional transfer functions. Besides flexibility and accuracy, a key
benefit of these models is their interpretability: the transfer functions
provide visual means for inspecting the models and identifying domain-specific
relations between inputs and outputs. However, in large-scale problems
involving the prediction of many related tasks, learning independently additive
models results in a loss of model interpretability, and can cause overfitting
when training data is scarce. We introduce a novel multi-task learning approach
which provides a corpus of accurate and interpretable additive models for a
large number of related forecasting tasks. Our key idea is to share transfer
functions across models in order to reduce the model complexity and ease the
exploration of the corpus. We establish a connection with sparse dictionary
learning and propose a new efficient fitting algorithm which alternates between
sparse coding and transfer function updates. The former step is solved via an
extension of Orthogonal Matching Pursuit, whose properties are analyzed using a
novel recovery condition which extends existing results in the literature. The
latter step is addressed using a traditional dictionary update rule.
Experiments on real-world data demonstrate that our approach compares favorably
to baseline methods while yielding an interpretable corpus of models, revealing
structure among the individual tasks and being more robust when training data
is scarce. Our framework therefore extends the well-known benefits of additive
models to common regression settings possibly involving thousands of tasks
Smart Grid Technologies in Europe: An Overview
The old electricity network infrastructure has proven to be inadequate, with respect to modern challenges such as alternative energy sources, electricity demand and energy saving policies. Moreover, Information and Communication Technologies (ICT) seem to have reached an adequate level of reliability and flexibility in order to support a new concept of electricity networkāthe smart grid. In this work, we will analyse the state-of-the-art of smart grids, in their technical, management, security, and optimization aspects. We will also provide a brief overview of the regulatory aspects involved in the development of a smart grid, mainly from the viewpoint of the European Unio
Local Short Term Electricity Load Forecasting: Automatic Approaches
Short-Term Load Forecasting (STLF) is a fundamental component in the
efficient management of power systems, which has been studied intensively over
the past 50 years. The emerging development of smart grid technologies is
posing new challenges as well as opportunities to STLF. Load data, collected at
higher geographical granularity and frequency through thousands of smart
meters, allows us to build a more accurate local load forecasting model, which
is essential for local optimization of power load through demand side
management. With this paper, we show how several existing approaches for STLF
are not applicable on local load forecasting, either because of long training
time, unstable optimization process, or sensitivity to hyper-parameters.
Accordingly, we select five models suitable for local STFL, which can be
trained on different time-series with limited intervention from the user. The
experiment, which consists of 40 time-series collected at different locations
and aggregation levels, revealed that yearly pattern and temperature
information are only useful for high aggregation level STLF. On local STLF
task, the modified version of double seasonal Holt-Winter proposed in this
paper performs relatively well with only 3 months of training data, compared to
more complex methods
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