497 research outputs found
short term electric load forecasting using echo state networks and pca decomposition
In this paper, we approach the problem of forecasting a time series (TS) of an electrical load measured on the Azienda Comunale Energia e Ambiente (ACEA) power grid, the company managing the electricity distribution in Rome, Italy, with an echo state network (ESN) considering two different leading times of 10 min and 1 day. We use a standard approach for predicting the load in the next 10 min, while, for a forecast horizon of one day, we represent the data with a high-dimensional multi-variate TS, where the number of variables is equivalent to the quantity of measurements registered in a day. Through the orthogonal transformation returned by PCA decomposition, we reduce the dimensionality of the TS to a lower number of distinct variables; this allows us to cast the original prediction problem in different one-step ahead predictions. The overall forecast can be effectively managed by distinct prediction models, whose outputs are combined together to obtain the final result. We employ a genetic algorithm for tuning the parameters of the ESN and compare its prediction accuracy with a standard autoregressive integrated moving average model
Training Echo State Networks with Regularization through Dimensionality Reduction
In this paper we introduce a new framework to train an Echo State Network to
predict real valued time-series. The method consists in projecting the output
of the internal layer of the network on a space with lower dimensionality,
before training the output layer to learn the target task. Notably, we enforce
a regularization constraint that leads to better generalization capabilities.
We evaluate the performances of our approach on several benchmark tests, using
different techniques to train the readout of the network, achieving superior
predictive performance when using the proposed framework. Finally, we provide
an insight on the effectiveness of the implemented mechanics through a
visualization of the trajectory in the phase space and relying on the
methodologies of nonlinear time-series analysis. By applying our method on well
known chaotic systems, we provide evidence that the lower dimensional embedding
retains the dynamical properties of the underlying system better than the
full-dimensional internal states of the network
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
Probabilistic load forecasting with Reservoir Computing
Some applications of deep learning require not only to provide accurate
results but also to quantify the amount of confidence in their prediction. The
management of an electric power grid is one of these cases: to avoid risky
scenarios, decision-makers need both precise and reliable forecasts of, for
example, power loads. For this reason, point forecasts are not enough hence it
is necessary to adopt methods that provide an uncertainty quantification.
This work focuses on reservoir computing as the core time series forecasting
method, due to its computational efficiency and effectiveness in predicting
time series. While the RC literature mostly focused on point forecasting, this
work explores the compatibility of some popular uncertainty quantification
methods with the reservoir setting. Both Bayesian and deterministic approaches
to uncertainty assessment are evaluated and compared in terms of their
prediction accuracy, computational resource efficiency and reliability of the
estimated uncertainty, based on a set of carefully chosen performance metrics
Probabilistic Wind Power and Electricity Load Forecasting with Echo State Networks
With the introduction of distributed generation and the establishment of smart grids,
several new challenges in energy analytics arose. These challenges can be solved with a
specific type of recurrent neural networks called echo state networks, which can handle
the combination of both weather and power consumption or production depending on the
dataset to make predictions. Echo state networks are particularly suitable for time series
forecasting tasks. Having accurate energy forecasts is paramount to assure grid operation
and power provision remains reliable during peak hours when the consumption is high.
The majority of load forecasting algorithms do not produce prediction intervals with
coverage guarantees but rather produce simple point estimates. Information about uncer-
tainty and prediction intervals is rarely useless. It helps grid operators change strategies
for configuring the grid from conservative to risk-based ones and assess the reliability of
operations.
A popular way of producing prediction intervals in regression tasks is by applying Bayesian
regression as the regression algorithm. As Bayesian regression is done by sampling, it nat-
urally lends itself to generating intervals. However, Bayesian regression is not guaranteed
to satisfy the designed coverage level for finite samples.
This thesis aims to modify the traditional echo state network model to produce marginally
valid and calibrated prediction intervals. This is done by replacing the standard linear
regression method with Bayesian linear regression while simultaneously reducing the di-
mensions to speed up the computation times. Afterward, a novel calibration technique
for time series forecasting is applied in order to obtain said valid prediction intervals.
The experiments are conducted using three different time series, two of them being a time
series of electricity load. One is univariate, and the other is bivariate. The third time series
is a wind power production time series. The proposed method showed promising results
for all three datasets while significantly reducing computation times in the sampling ste
A review of the enabling methodologies for knowledge discovery from smart grids data
The large-scale deployment of pervasive sensors and decentralized computing in modern smart
grids is expected to exponentially increase the volume of data exchanged by power system applications.
In this context, the research for scalable and flexible methodologies aimed at supporting rapid decisions
in a data rich, but information limited environment represents a relevant issue to address. To this aim,
this paper investigates the role of Knowledge Discovery from massive Datasets in smart grid computing,
exploring its various application fields by considering the power system stakeholder available data and
knowledge extraction needs. In particular, the aim of this paper is dual. In the first part, the authors
summarize the most recent activities developed in this field by the Task Force on “Enabling Paradigms
for High-Performance Computing in Wide Area Monitoring Protective and Control Systems” of the
IEEE PSOPE Technologies and Innovation Subcommittee. Differently, in the second part, the authors
propose the development of a data-driven forecasting methodology, which is modeled by considering
the fundamental principles of Knowledge Discovery Process data workflow. Furthermore, the described
methodology is applied to solve the load forecasting problem for a complex user case, in order to
emphasize the potential role of knowledge discovery in supporting post processing analysis in data-rich
environments, as feedback for the improvement of the forecasting performances
Reservoir computing approaches for representation and classification of multivariate time series
Classification of multivariate time series (MTS) has been tackled with a
large variety of methodologies and applied to a wide range of scenarios.
Reservoir Computing (RC) provides efficient tools to generate a vectorial,
fixed-size representation of the MTS that can be further processed by standard
classifiers. Despite their unrivaled training speed, MTS classifiers based on a
standard RC architecture fail to achieve the same accuracy of fully trainable
neural networks. In this paper we introduce the reservoir model space, an
unsupervised approach based on RC to learn vectorial representations of MTS.
Each MTS is encoded within the parameters of a linear model trained to predict
a low-dimensional embedding of the reservoir dynamics. Compared to other RC
methods, our model space yields better representations and attains comparable
computational performance, thanks to an intermediate dimensionality reduction
procedure. As a second contribution we propose a modular RC framework for MTS
classification, with an associated open-source Python library. The framework
provides different modules to seamlessly implement advanced RC architectures.
The architectures are compared to other MTS classifiers, including deep
learning models and time series kernels. Results obtained on benchmark and
real-world MTS datasets show that RC classifiers are dramatically faster and,
when implemented using our proposed representation, also achieve superior
classification accuracy
Industrial time series modelling by means of the neo-fuzzy neuron
Abstract—Industrial process monitoring and modelling represents a critical step in order to achieve the paradigm of Zero Defect Manufacturing. The aim of this paper is to introduce the Neo-Fuzzy Neuron method to be applied in industrial time series modelling. Its open structure and input independency provides fast learning and convergence capabilities, while assuring a proper accuracy and generalization in the modelled output. First, the auxiliary signals in the database are analyzed in order to find correlations with the target signal. Second, the Neo-Fuzzy Neuron is configured and trained according by means of the auxiliary signal, past instants and dynamics information of the target signal. The proposed method is validated by means of real data from a Spanish copper rod industrial plant, in which a critical signal regarding copper refrigeration process is modelled. The obtained results indicate the suitability of the Neo-Fuzzy Neuron method for industrial process modelling.Postprint (published version
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