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Publication
Load forecasting: a cross-field study on server and energy load forecasting Impact of temporal factors on generalization ability and performance of regression models
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Authors
Abstract(s)
The server load prediction and energy load forecasting have available a wide range of
approaches and applications, with their general goal being the prediction of future load
for a specific period of time on a given system. Depending on the specific goal, different
methodologies can be applied.
In this dissertation, the integration of additional temporal information to datasets, as
a mean to create a more generalized model is studied. The main steps involve a deep
literature review in order to find the most suited methodologies and/or learning methods.
A novel dataset enrichment process through the integration of extra temporal information
and lastly, a cross-model testing stage, where trained models for server load prediction and
energy load forecast are applied to the opposite field. This last stage, tests and analyses the
generalization level of the created models through the temporal information integration
procedure.
The created models were both oriented to short-term load forecasting problems, with
the use of data from single and combined months, regarding real data from Wikipedia
servers of the year 2016 in the case of server load prediction and real data regarding
the consumption levels in April 2016 of the city of Leiria/Portugal for the energy load
forecasting case study. The learning methods used for creating the different models
were linear regression, artificial neural networks and support vector machines oriented
to regression problems, more precisely the Smoreg implementation. Results prove that
it is possible to tune the dataset features, e.g., granularity and time window to improve
prediction results and generalization. Results from this work, as well as an optimization
approach through the use of genetic algorithms, normalization effects, split ratio vs crossvalidation
influence and different granularities and time windows were peer-reviewed
published.
Description
Keywords
Load prediction Server load prediction Energy load forecasting Regression problems Linear regression Artificial neural networks Support vector machines Model generalization