Mr. Salah Dauga Profile

Salah Dauga

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 22 April 2020 Presentation + Paper

Artificial neural network approach to improve the performance of battery and thermal storage

Salah Dauga, Ibrahim Aldaouab

Proceedings Volume 11382, 113820G (2020) https://doi.org/10.1117/12.2556489

KEYWORDS: Renewable energy, Solar energy, Wind energy, Artificial neural networks, Solar cells, Neural networks, Photovoltaics, Control systems, Wind turbine technology, Thermal modeling

Read Abstract +

Energy storage technology and its efficient deployment will be increasingly needed to manage intermittent of renewable energy supply. Large scale storage can support a power grid, as with pumped hydroelectric, or small-scale battery systems can be used to support rooftop PV within a single building. This work considers a microgrid which supplies renewable energy (solar PV) to a single building with both electric (battery) and thermal (hot-water) energy storage. The goal is to explore the potential benefit of using artificial neural network with model productive control algorithm to predict the load demand and energy supply in order to lower the cost of storage system to assist in managing renewable power fluctuations, which is appropriate when significant thermal loads are present. The building considered here contains of apartments, and hourly electrical consumption and thermal demand are developed from historical meter data. Renewable energy from a PV array is dispatched to the load or is stored for later use, and the microgrid performance is measured by the renewable energy penetration, renewable curtailment, and system cost over time. Modeling results indicate that predict the load demand supply lower the cost of the energy. Also, increasing renewable energy penetration and decrease renewable energy curtailment.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years