66,957 research outputs found
Deep Learning for Energy Markets
Deep Learning is applied to energy markets to predict extreme loads observed
in energy grids. Forecasting energy loads and prices is challenging due to
sharp peaks and troughs that arise due to supply and demand fluctuations from
intraday system constraints. We propose deep spatio-temporal models and extreme
value theory (EVT) to capture theses effects and in particular the tail
behavior of load spikes. Deep LSTM architectures with ReLU and
activation functions can model trends and temporal dependencies while EVT
captures highly volatile load spikes above a pre-specified threshold. To
illustrate our methodology, we use hourly price and demand data from 4719 nodes
of the PJM interconnection, and we construct a deep predictor. We show that
DL-EVT outperforms traditional Fourier time series methods, both in-and
out-of-sample, by capturing the observed nonlinearities in prices. Finally, we
conclude with directions for future research
Comparison of Classical and Nonlinear Models for Short-Term Electricity Price Prediction
Electricity is bought and sold in wholesale markets at prices that fluctuate
significantly. Short-term forecasting of electricity prices is an important
endeavor because it helps electric utilities control risk and because it
influences competitive strategy for generators. As the "smart grid" grows,
short-term price forecasts are becoming an important input to bidding and
control algorithms for battery operators and demand response aggregators. While
the statistics and machine learning literature offers many proposed methods for
electricity price prediction, there is no consensus supporting a single best
approach. We test two contrasting machine learning approaches for predicting
electricity prices, regression decision trees and recurrent neural networks
(RNNs), and compare them to a more traditional ARIMA implementation. We conduct
the analysis on a challenging dataset of electricity prices from ERCOT, in
Texas, where price fluctuation is especially high. We find that regression
decision trees in particular achieves high performance compared to the other
methods, suggesting that regression trees should be more carefully considered
for electricity price forecasting
Big Data Analytics for Dynamic Energy Management in Smart Grids
The smart electricity grid enables a two-way flow of power and data between
suppliers and consumers in order to facilitate the power flow optimization in
terms of economic efficiency, reliability and sustainability. This
infrastructure permits the consumers and the micro-energy producers to take a
more active role in the electricity market and the dynamic energy management
(DEM). The most important challenge in a smart grid (SG) is how to take
advantage of the users' participation in order to reduce the cost of power.
However, effective DEM depends critically on load and renewable production
forecasting. This calls for intelligent methods and solutions for the real-time
exploitation of the large volumes of data generated by a vast amount of smart
meters. Hence, robust data analytics, high performance computing, efficient
data network management, and cloud computing techniques are critical towards
the optimized operation of SGs. This research aims to highlight the big data
issues and challenges faced by the DEM employed in SG networks. It also
provides a brief description of the most commonly used data processing methods
in the literature, and proposes a promising direction for future research in
the field.Comment: Published in ELSEVIER Big Data Researc
A Sparse Linear Model and Significance Test for Individual Consumption Prediction
Accurate prediction of user consumption is a key part not only in
understanding consumer flexibility and behavior patterns, but in the design of
robust and efficient energy saving programs as well. Existing prediction
methods usually have high relative errors that can be larger than 30% and have
difficulties accounting for heterogeneity between individual users. In this
paper, we propose a method to improve prediction accuracy of individual users
by adaptively exploring sparsity in historical data and leveraging predictive
relationship between different users. Sparsity is captured by popular least
absolute shrinkage and selection estimator, while user selection is formulated
as an optimal hypothesis testing problem and solved via a covariance test.
Using real world data from PG&E, we provide extensive simulation validation of
the proposed method against well-known techniques such as support vector
machine, principle component analysis combined with linear regression, and
random forest. The results demonstrate that our proposed methods are
operationally efficient because of linear nature, and achieve optimal
prediction performance.Comment: 36 pages, 7 figure
Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges
The widespread popularity of smart meters enables an immense amount of
fine-grained electricity consumption data to be collected. Meanwhile, the
deregulation of the power industry, particularly on the delivery side, has
continuously been moving forward worldwide. How to employ massive smart meter
data to promote and enhance the efficiency and sustainability of the power grid
is a pressing issue. To date, substantial works have been conducted on smart
meter data analytics. To provide a comprehensive overview of the current
research and to identify challenges for future research, this paper conducts an
application-oriented review of smart meter data analytics. Following the three
stages of analytics, namely, descriptive, predictive and prescriptive
analytics, we identify the key application areas as load analysis, load
forecasting, and load management. We also review the techniques and
methodologies adopted or developed to address each application. In addition, we
also discuss some research trends, such as big data issues, novel machine
learning technologies, new business models, the transition of energy systems,
and data privacy and security.Comment: IEEE Transactions on Smart Grid, 201
Household Electricity Demand Forecasting -- Benchmarking State-of-the-Art Methods
The increasing use of renewable energy sources with variable output, such as
solar photovoltaic and wind power generation, calls for Smart Grids that
effectively manage flexible loads and energy storage. The ability to forecast
consumption at different locations in distribution systems will be a key
capability of Smart Grids. The goal of this paper is to benchmark
state-of-the-art methods for forecasting electricity demand on the household
level across different granularities and time scales in an explorative way,
thereby revealing potential shortcomings and find promising directions for
future research in this area. We apply a number of forecasting methods
including ARIMA, neural networks, and exponential smoothening using several
strategies for training data selection, in particular day type and sliding
window based strategies. We consider forecasting horizons ranging between 15
minutes and 24 hours. Our evaluation is based on two data sets containing the
power usage of individual appliances at second time granularity collected over
the course of several months. The results indicate that forecasting accuracy
varies significantly depending on the choice of forecasting methods/strategy
and the parameter configuration. Measured by the Mean Absolute Percentage Error
(MAPE), the considered state-of-the-art forecasting methods rarely beat
corresponding persistence forecasts. Overall, we observed MAPEs in the range
between 5 and >100%. The average MAPE for the first data set was ~30%, while it
was ~85% for the other data set. These results show big room for improvement.
Based on the identified trends and experiences from our experiments, we
contribute a detailed discussion of promising future research.Comment: Technical Repor
Sequence Classification of the Limit Order Book using Recurrent Neural Networks
Recurrent neural networks (RNNs) are types of artificial neural networks
(ANNs) that are well suited to forecasting and sequence classification. They
have been applied extensively to forecasting univariate financial time series,
however their application to high frequency trading has not been previously
considered. This paper solves a sequence classification problem in which a
short sequence of observations of limit order book depths and market orders is
used to predict a next event price-flip. The capability to adjust quotes
according to this prediction reduces the likelihood of adverse price selection.
Our results demonstrate the ability of the RNN to capture the non-linear
relationship between the near-term price-flips and a spatio-temporal
representation of the limit order book. The RNN compares favorably with other
classifiers, including a linear Kalman filter, using S&P500 E-mini futures
level II data over the month of August 2016. Further results assess the effect
of retraining the RNN daily and the sensitivity of the performance to trade
latency.Comment: arXiv admin note: text overlap with arXiv:1705.0985
Predicting digital asset market based on blockchain activity data
Blockchain technology shows significant results and huge potential for
serving as an interweaving fabric that goes through every industry and market,
allowing decentralized and secure value exchange, thus connecting our
civilization like never before. The standard approach for asset value
predictions is based on market analysis with an LSTM neural network. Blockchain
technologies, however, give us access to vast amounts of public data, such as
the executed transactions and the account balance distribution. We explore
whether analyzing this data with modern Deep Leaning techniques results in
higher accuracies than the standard approach. During a series of experiments on
the Ethereum blockchain, we achieved times error reduction with blockchain
data than an LSTM approach with trade volume data. By utilizing blockchain
account distribution histograms, spatial dataset modeling, and a Convolutional
architecture, the error was reduced further by 26\%. The proposed methodologies
are implemented in an open source cryptocurrency prediction framework, allowing
them to be used in other analysis contexts
Short Term Load Forecasting Using Deep Neural Networks
Electricity load forecasting plays an important role in the energy planning
such as generation and distribution. However, the nonlinearity and dynamic
uncertainties in the smart grid environment are the main obstacles in
forecasting accuracy. Deep Neural Network (DNN) is a set of intelligent
computational algorithms that provide a comprehensive solution for modelling a
complicated nonlinear relationship between the input and output through
multiple hidden layers. In this paper, we propose DNN based electricity load
forecasting system to manage the energy consumption in an efficient manner. We
investigate the applicability of two deep neural network architectures
Feed-forward Deep Neural Network (Deep-FNN) and Recurrent Deep Neural Network
(Deep-RNN) to the New York Independent System Operator (NYISO) electricity load
forecasting task. We test our algorithm with various activation functions such
as Sigmoid, Hyperbolic Tangent (tanh) and Rectifier Linear Unit (ReLU). The
performance measurement of two network architectures is compared in terms of
Mean Absolute Percentage Error (MAPE) metric.Comment: 6 pages, 8 figures, International Symposium on Information Technology
Convergence 2018, South Kore
Multivariate Ensemble Forecast Framework for Demand Prediction of Anomalous Days
An accurate load forecast is always important for the power industry and
energy players as it enables stakeholders to make critical decisions. In
addition, its importance is further increased with growing uncertainties in the
generation sector due to the high penetration of renewable energy and the
introduction of demand side management strategies. An incremental improvement
in grid-level demand forecast of anomalous days can potentially save millions
of dollars. However, due to an increasing penetration of renewable energy
resources and their dependency on several meteorological and exogenous
variables, accurate load forecasting of anomalous days has now become very
challenging. To improve the prediction accuracy of the load forecasting, an
ensemble forecast framework (ENFF) is proposed with a systematic combination of
three multiple predictors, namely Elman neural network (ELM), feedforward
neural network (FNN) and radial basis function (RBF) neural network. These
predictors are trained using global particle swarm optimization (GPSO) to
improve their prediction capability in the ENFF. The outputs of individual
predictors are combined using a trim aggregation technique by removing
forecasting anomalies. Real recorded data of New England ISO grid is used for
training and testing of the ENFF for anomalous days. The forecast results of
the proposed ENFF indicate a significant improvement in prediction accuracy in
comparison to autoregressive integrated moving average (ARIMA) and
back-propagation neural networks (BPNN) based benchmark models
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