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AuthorAyoub N.
AuthorMusharavati F.
AuthorPokharel S.
AuthorGabbar H.A.
Available date2020-02-24T08:57:14Z
Publication Date2018
Publication Name2018 6th IEEE International Conference on Smart Energy Grid Engineering, SEGE 2018
ResourceScopus
URIhttp://dx.doi.org/10.1109/SEGE.2018.8499514
URIhttp://hdl.handle.net/10576/13014
AbstractThis paper presents short term demand and supply forecasting model for a microgrid supply system used to secure the electricity demands of a commercial building, using one year demand data collected in hourly base. One-year renewable-based Micro-grid electricity supply data were produced by simulating its sub-systems (wind and PV supply systems). The Artificial Neural Network, ANN, forecasting models are built on predicting generation capacity and load demands in the next 24 hours. The ANN model presented here is a micro-level supply and demand forecasting model that links the decision making with the performance measures. To sustain the model results, the daily weather forecasts supplied by local authorities, are incorporated in our model. The models validity were tested by calculating the Mean Absolute Percent Error for the forecasted data. The ANN models' applicability and performance were tested in a case study for forecasting the demands of a hotel building and the supply potential of its microgrid supply sub-system. The building demands are assumed to be supplied by a hybrid supply system of 20% renewable-based Micro grid (10% Wind and 10% Photovoltaic) and 80% from electricity grid.
SponsorThis research was made possible by a NPRP award NPRP 5 ? 209 ? 2 - 071 from the Qatar National Research Fund (a member of The Qatar Foundation). The statements made herein are solely the responsibility of the authors.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectCommercial
Energy conservation
Forecasting
Hybrid supply system
Neural network
TitleANN Model for Energy Demand and Supply Forecasting in a Hybrid Energy Supply System
TypeConference Paper
Pagination25 - 30
dc.accessType Abstract Only


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