9,784 research outputs found
Development of Neurofuzzy Architectures for Electricity Price Forecasting
In 20th century, many countries have liberalized their electricity market. This power markets liberalization has directed generation companies as well as wholesale buyers to undertake a greater intense risk exposure compared to the old centralized framework. In this framework, electricity price prediction has become crucial for any market player in their decisionâmaking process as well as strategic planning. In this study, a prototype asymmetricâbased neuroâfuzzy network (AGFINN) architecture has been implemented for shortâterm electricity prices forecasting for ISO New England market. AGFINN framework has been designed through two different defuzzification schemes. Fuzzy clustering has been explored as an initial step for defining the fuzzy rules while an asymmetric Gaussian membership function has been utilized in the fuzzification part of the model. Results related to the minimum and maximum electricity prices for ISO New England, emphasize the superiority of the proposed model over wellâestablished learningâbased models
Spatio-Temporal Low Count Processes with Application to Violent Crime Events
There is significant interest in being able to predict where crimes will
happen, for example to aid in the efficient tasking of police and other
protective measures. We aim to model both the temporal and spatial dependencies
often exhibited by violent crimes in order to make such predictions. The
temporal variation of crimes typically follows patterns familiar in time series
analysis, but the spatial patterns are irregular and do not vary smoothly
across the area. Instead we find that spatially disjoint regions exhibit
correlated crime patterns. It is this indeterminate inter-region correlation
structure along with the low-count, discrete nature of counts of serious crimes
that motivates our proposed forecasting tool. In particular, we propose to
model the crime counts in each region using an integer-valued first order
autoregressive process. We take a Bayesian nonparametric approach to flexibly
discover a clustering of these region-specific time series. We then describe
how to account for covariates within this framework. Both approaches adjust for
seasonality. We demonstrate our approach through an analysis of weekly reported
violent crimes in Washington, D.C. between 2001-2008. Our forecasts outperform
standard methods while additionally providing useful tools such as prediction
intervals
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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