5,400 research outputs found
Input variable selection for forecasting models
2002 IFAC15th Triennial World Congress, Barcelona, SpainThe selection of input variables plays a crucial role when modelling time series. For nonlinear models there are not well developed techniques such as AIC and other criteria that work with linear models. In the case of Short Term Load Forecasting (STLF) generalization is greatly influenced by such selection. In this paper two approaches are compared using real data from a Spanish utility company. The models used are neural networks although the algorithms can be used with other nonlinear models. The experiments show that that input variable selection affects the performance of forecasting models and thus should be treated as a generalization problem
An Unsupervised Deep Learning Approach for Scenario Forecasts
In this paper, we propose a novel scenario forecasts approach which can be
applied to a broad range of power system operations (e.g., wind, solar, load)
over various forecasts horizons and prediction intervals. This approach is
model-free and data-driven, producing a set of scenarios that represent
possible future behaviors based only on historical observations and point
forecasts. It first applies a newly-developed unsupervised deep learning
framework, the generative adversarial networks, to learn the intrinsic patterns
in historical renewable generation data. Then by solving an optimization
problem, we are able to quickly generate large number of realistic future
scenarios. The proposed method has been applied to a wind power generation and
forecasting dataset from national renewable energy laboratory. Simulation
results indicate our method is able to generate scenarios that capture spatial
and temporal correlations. Our code and simulation datasets are freely
available online.Comment: Accepted to Power Systems Computation Conference 2018 Code available
at https://github.com/chennnnnyize/Scenario-Forecasts-GA
Short-Term Load Forecasting of Natural Gas with Deep Neural Network Regression
Deep neural networks are proposed for short-term natural gas load forecasting. Deep learning has proven to be a powerful tool for many classification problems seeing significant use in machine learning fields such as image recognition and speech processing. We provide an overview of natural gas forecasting. Next, the deep learning method, contrastive divergence is explained. We compare our proposed deep neural network method to a linear regression model and a traditional artificial neural network on 62 operating areas, each of which has at least 10 years of data. The proposed deep network outperforms traditional artificial neural networks by 9.83% weighted mean absolute percent error (WMAPE)
An Integrated Multi-Time-Scale Modeling for Solar Irradiance Forecasting Using Deep Learning
For short-term solar irradiance forecasting, the traditional point
forecasting methods are rendered less useful due to the non-stationary
characteristic of solar power. The amount of operating reserves required to
maintain reliable operation of the electric grid rises due to the variability
of solar energy. The higher the uncertainty in the generation, the greater the
operating-reserve requirements, which translates to an increased cost of
operation. In this research work, we propose a unified architecture for
multi-time-scale predictions for intra-day solar irradiance forecasting using
recurrent neural networks (RNN) and long-short-term memory networks (LSTMs).
This paper also lays out a framework for extending this modeling approach to
intra-hour forecasting horizons thus, making it a multi-time-horizon
forecasting approach, capable of predicting intra-hour as well as intra-day
solar irradiance. We develop an end-to-end pipeline to effectuate the proposed
architecture. The performance of the prediction model is tested and validated
by the methodical implementation. The robustness of the approach is
demonstrated with case studies conducted for geographically scattered sites
across the United States. The predictions demonstrate that our proposed unified
architecture-based approach is effective for multi-time-scale solar forecasts
and achieves a lower root-mean-square prediction error when benchmarked against
the best-performing methods documented in the literature that use separate
models for each time-scale during the day. Our proposed method results in a
71.5% reduction in the mean RMSE averaged across all the test sites compared to
the ML-based best-performing method reported in the literature. Additionally,
the proposed method enables multi-time-horizon forecasts with real-time inputs,
which have a significant potential for practical industry applications in the
evolving grid.Comment: 19 pages, 12 figures, 3 tables, under review for journal submissio
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