Off-campus UMass Amherst users: To download campus access dissertations, please use the following link to log into our proxy server with your UMass Amherst user name and password.
Non-UMass Amherst users: Please talk to your librarian about requesting this dissertation through interlibrary loan.
Dissertations that have an embargo placed on them will not be available to anyone until the embargo expires.
ORCID
https://orcid.org/0000-0001-7832-1099
Access Type
Open Access Thesis
Document Type
thesis
Degree Program
Mechanical Engineering
Degree Type
Master of Science in Mechanical Engineering (M.S.M.E.)
Year Degree Awarded
2020
Month Degree Awarded
September
Abstract
Decentralization of the electric grid can increase resiliency (during natural disasters) and can reduce T&D energy losses and emissions. Microgrids and DERs can enable this to happen. It is important to optimally control microgrids and DERs to extract the greatest economic, environmental and resiliency benefits. This is enabled by robust forecasting to optimally control loads and energy sources. An integral part of microgrid control is power side and load side demand forecasting.
In this thesis, we look at the ability of a powerful neural network algorithm to forecast the load side demand for a microgrid using the UMass campus as the test bed. UMass has its own power plant producing 16 MW of power. In addition to this, Solar panels totaling 5.5MW and lithium ion battery bank of 1.32 MW/4 MWh are also available. An LSTM recurrent neural network is used for demand forecasting. In addition to a fully trained LSTM network, multi linear regression model, ARIMA and ANN model are also tested to compare the performance.
In addition to the Short Term Load Forecasting, the peak prediction accuracy of the model was also tested to run a battery discharge algorithm to shave peak demand for the microgrid. This will result in demand cost savings for the facility. Finally, the fully trained neural network was deployed on a raspberry pi computer.
DOI
https://doi.org/10.7275/18972960
First Advisor
Dragoljub Kosanovic
Second Advisor
Prashant Shenoy
Third Advisor
Hari Balasubramanian
Fourth Advisor
Benjamin McDaniel
Recommended Citation
Soman, Akhil, "Short Term Energy Forecasting for a Microgird Load using LSTM RNN" (2020). Masters Theses. 994.
https://doi.org/10.7275/18972960
https://scholarworks.umass.edu/masters_theses_2/994