19,534 research outputs found
Learning and Optimization with Seasonal Patterns
Seasonality is a common form of non-stationary patterns in the business
world. We study a decision maker who tries to learn the optimal decision over
time when the environment is unknown and evolving with seasonality. We consider
a multi-armed bandit (MAB) framework where the mean rewards are periodic. The
unknown periods of the arms can be different and scale with the length of the
horizon polynomially. We propose a two-staged policy that combines Fourier
analysis with a confidence-bound based learning procedure to learn the periods
and minimize the regret. In stage one, the policy is able to correctly estimate
the periods of all arms with high probability. In stage two, the policy
explores mean rewards of arms in each phase using the periods estimated in
stage one and exploits the optimal arm in the long run. We show that our policy
achieves the rate of regret , where is
the number of arms and is the period of arm . It matches the optimal
rate of regret of the classic MAB problem if we regard each
phase of an arm as a separate arm
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
PSO based Neural Networks vs. Traditional Statistical Models for Seasonal Time Series Forecasting
Seasonality is a distinctive characteristic which is often observed in many
practical time series. Artificial Neural Networks (ANNs) are a class of
promising models for efficiently recognizing and forecasting seasonal patterns.
In this paper, the Particle Swarm Optimization (PSO) approach is used to
enhance the forecasting strengths of feedforward ANN (FANN) as well as Elman
ANN (EANN) models for seasonal data. Three widely popular versions of the basic
PSO algorithm, viz. Trelea-I, Trelea-II and Clerc-Type1 are considered here.
The empirical analysis is conducted on three real-world seasonal time series.
Results clearly show that each version of the PSO algorithm achieves notably
better forecasting accuracies than the standard Backpropagation (BP) training
method for both FANN and EANN models. The neural network forecasting results
are also compared with those from the three traditional statistical models,
viz. Seasonal Autoregressive Integrated Moving Average (SARIMA), Holt-Winters
(HW) and Support Vector Machine (SVM). The comparison demonstrates that both
PSO and BP based neural networks outperform SARIMA, HW and SVM models for all
three time series datasets. The forecasting performances of ANNs are further
improved through combining the outputs from the three PSO based models.Comment: 4 figures, 4 tables, 31 references, conference proceeding
Wind Power Forecasting Methods Based on Deep Learning: A Survey
Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid. Aiming to provide reference strategies for relevant researchers as well as practical applications, this paper attempts to provide the literature investigation and methods analysis of deep learning, enforcement learning and transfer learning in wind speed and wind power forecasting modeling. Usually, wind speed and wind power forecasting around a wind farm requires the calculation of the next moment of the definite state, which is usually achieved based on the state of the atmosphere that encompasses nearby atmospheric pressure, temperature, roughness, and obstacles. As an effective method of high-dimensional feature extraction, deep neural network can theoretically deal with arbitrary nonlinear transformation through proper structural design, such as adding noise to outputs, evolutionary learning used to optimize hidden layer weights, optimize the objective function so as to save information that can improve the output accuracy while filter out the irrelevant or less affected information for forecasting. The establishment of high-precision wind speed and wind power forecasting models is always a challenge due to the randomness, instantaneity and seasonal characteristics
SHOPPER: A Probabilistic Model of Consumer Choice with Substitutes and Complements
We develop SHOPPER, a sequential probabilistic model of shopping data.
SHOPPER uses interpretable components to model the forces that drive how a
customer chooses products; in particular, we designed SHOPPER to capture how
items interact with other items. We develop an efficient posterior inference
algorithm to estimate these forces from large-scale data, and we analyze a
large dataset from a major chain grocery store. We are interested in answering
counterfactual queries about changes in prices. We found that SHOPPER provides
accurate predictions even under price interventions, and that it helps identify
complementary and substitutable pairs of products.Comment: Published at Annals of Applied Statistics. 27 pages, 4 figure
Submodular Load Clustering with Robust Principal Component Analysis
Traditional load analysis is facing challenges with the new electricity usage
patterns due to demand response as well as increasing deployment of distributed
generations, including photovoltaics (PV), electric vehicles (EV), and energy
storage systems (ESS). At the transmission system, despite of irregular load
behaviors at different areas, highly aggregated load shapes still share similar
characteristics. Load clustering is to discover such intrinsic patterns and
provide useful information to other load applications, such as load forecasting
and load modeling. This paper proposes an efficient submodular load clustering
method for transmission-level load areas. Robust principal component analysis
(R-PCA) firstly decomposes the annual load profiles into low-rank components
and sparse components to extract key features. A novel submodular cluster
center selection technique is then applied to determine the optimal cluster
centers through constructed similarity graph. Following the selection results,
load areas are efficiently assigned to different clusters for further load
analysis and applications. Numerical results obtained from PJM load demonstrate
the effectiveness of the proposed approach.Comment: Accepted by 2019 IEEE PES General Meeting, Atlanta, G
Wireless Traffic Prediction with Scalable Gaussian Process: Framework, Algorithms, and Verification
The cloud radio access network (C-RAN) is a promising paradigm to meet the
stringent requirements of the fifth generation (5G) wireless systems.
Meanwhile, wireless traffic prediction is a key enabler for C-RANs to improve
both the spectrum efficiency and energy efficiency through load-aware network
managements. This paper proposes a scalable Gaussian process (GP) framework as
a promising solution to achieve large-scale wireless traffic prediction in a
cost-efficient manner. Our contribution is three-fold. First, to the best of
our knowledge, this paper is the first to empower GP regression with the
alternating direction method of multipliers (ADMM) for parallel hyper-parameter
optimization in the training phase, where such a scalable training framework
well balances the local estimation in baseband units (BBUs) and information
consensus among BBUs in a principled way for large-scale executions. Second, in
the prediction phase, we fuse local predictions obtained from the BBUs via a
cross-validation based optimal strategy, which demonstrates itself to be
reliable and robust for general regression tasks. Moreover, such a
cross-validation based optimal fusion strategy is built upon a well
acknowledged probabilistic model to retain the valuable closed-form GP
inference properties. Third, we propose a C-RAN based scalable wireless
prediction architecture, where the prediction accuracy and the time consumption
can be balanced by tuning the number of the BBUs according to the real-time
system demands. Experimental results show that our proposed scalable GP model
can outperform the state-of-the-art approaches considerably, in terms of
wireless traffic prediction performance
Model-Free Renewable Scenario Generation Using Generative Adversarial Networks
Scenario generation is an important step in the operation and planning of
power systems with high renewable penetrations. In this work, we proposed a
data-driven approach for scenario generation using generative adversarial
networks, which is based on two interconnected deep neural networks. Compared
with existing methods based on probabilistic models that are often hard to
scale or sample from, our method is data-driven, and captures renewable energy
production patterns in both temporal and spatial dimensions for a large number
of correlated resources. For validation, we use wind and solar times-series
data from NREL integration data sets. We demonstrate that the proposed method
is able to generate realistic wind and photovoltaic power profiles with full
diversity of behaviors. We also illustrate how to generate scenarios based on
different conditions of interest by using labeled data during training. For
example, scenarios can be conditioned on weather events~(e.g. high wind day) or
time of the year~(e,g. solar generation for a day in July). Because of the
feedforward nature of the neural networks, scenarios can be generated extremely
efficiently without sophisticated sampling techniques.Comment: Accepted to IEEE Transactions on Power Systems; code available at
https://github.com/chennnnnyize/Renewables_Scenario_Gen_GA
Demand Forecasting from Spatiotemporal Data with Graph Networks and Temporal-Guided Embedding
Short-term demand forecasting models commonly combine convolutional and
recurrent layers to extract complex spatiotemporal patterns in data. Long-term
histories are also used to consider periodicity and seasonality patterns as
time series data. In this study, we propose an efficient architecture,
Temporal-Guided Network (TGNet), which utilizes graph networks and
temporal-guided embedding. Graph networks extract invariant features to
permutations of adjacent regions instead of convolutional layers.
Temporal-guided embedding explicitly learns temporal contexts from training
data and is substituted for the input of long-term histories from days/weeks
ago. TGNet learns an autoregressive model, conditioned on temporal contexts of
forecasting targets from temporal-guided embedding. Finally, our model achieves
competitive performances with other baselines on three spatiotemporal demand
dataset from real-world, but the number of trainable parameters is about 20
times smaller than a state-of-the-art baseline. We also show that
temporal-guided embedding learns temporal contexts as intended and TGNet has
robust forecasting performances even to atypical event situations.Comment: NeurIPS 2018 Workshop on Modeling and Decision-Making in the
Spatiotemporal Domai
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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