18,818 research outputs found
Using high-frequency data and time series models to improve yield management
We show the potential contribution of time series models (TSM) to the analysis of high frequency (less than monthly) time series of economic activity. The evolution of the series is induced by stable patterns of behavior of economic agents; but these patterns are so complex that simple smoothing techniques or subjective forecasting can not consider all underlying factors and TSM are needed if a full efficient analysis is to be carried out. The main ideas are illustrated with an apllication to Spanish daily electricity consumption
Application of Deep Learning Long Short-Term Memory in Energy Demand Forecasting
The smart metering infrastructure has changed how electricity is measured in
both residential and industrial application. The large amount of data collected
by smart meter per day provides a huge potential for analytics to support the
operation of a smart grid, an example of which is energy demand forecasting.
Short term energy forecasting can be used by utilities to assess if any
forecasted peak energy demand would have an adverse effect on the power system
transmission and distribution infrastructure. It can also help in load
scheduling and demand side management. Many techniques have been proposed to
forecast time series including Support Vector Machine, Artificial Neural
Network and Deep Learning. In this work we use Long Short Term Memory
architecture to forecast 3-day ahead energy demand across each month in the
year. The results show that 3-day ahead demand can be accurately forecasted
with a Mean Absolute Percentage Error of 3.15%. In addition to that, the paper
proposes way to quantify the time as a feature to be used in the training phase
which is shown to affect the network performance
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An Assessment of PIER Electric Grid Research 2003-2014 White Paper
This white paper describes the circumstances in California around the turn of the 21st century that led the California Energy Commission (CEC) to direct additional Public Interest Energy Research funds to address critical electric grid issues, especially those arising from integrating high penetrations of variable renewable generation with the electric grid. It contains an assessment of the beneficial science and technology advances of the resultant portfolio of electric grid research projects administered under the direction of the CEC by a competitively selected contractor, the University of California’s California Institute for Energy and the Environment, from 2003-2014
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Photovoltaic and Behind-the-Meter Battery Storage: Advanced Smart Inverter Controls and Field Demonstration
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