ANN-Based Scenario Generation Approach for Energy Management of Smart Buildings

Ladataan...
nbnfi-fe2026021112644.pdf
Hyväksytty kirjoittajan käsikirjoitus - 1.49 MB
Huom! Tiedosto avautuu julkiseksi: 26.02.2027

Kuvaus

© 2025 The Institute of Electrical and Electronics Engineers, Inc. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
The growing trend of integrating renewable energy generation (e.g., PV) and electric vehicle charging solutions in buildings in addition to increasing electrification of demand (e.g., heating) requires an efficient Building Energy Management System (BEMS). BEMS is needed to manage the operation of controllable distributed energy resources (DER) in buildings according to the priorities of the building owner, such as maximizing own renewable generation utilization or minimizing the total energy costs or maximizing customer satisfaction and comfort-related needs. Related to the management of DER units, the uncertainty of renewable energy resources is a big challenge. To tackle the challenge resulting from uncertain renewable generation artificial intelligence can be employed to increase the prediction accuracy of intermittent weather-dependent generation. In this regard, an artificial neural network (ANN) model can be used to generate multiple scenarios, for example, for PV generation and solar radiation forecasting based on historical data. Moreover, a two-stage stochastic model can be deployed to model the operation of smart buildings in the day-ahead and real-time stages. Using the scenarios generated by the ANN-based approach and the two-stage stochastic model the optimal operation of a smart building can be conducted to schedule the operating point of different assets such as PV system, battery energy storage, electric vehicle, and space heater. The developed model assists smart buildings in reducing their energy cost and encourages them to deploy more renewable energy resources as well as electric vehicles, and to participate actively in the demand response program.

Emojulkaisu

Smart Cyber-Physical Power Systems: Solutions from Emerging Technologies (Vol. 2)

ISBN

978-1-394-33459-9

ISSN

Aihealue

Kausijulkaisu

IEEE Press Series on Power and Energy Systems

OKM-julkaisutyyppi

A3 Kirjan tai muun kokoomateoksen osa (vertaisarvioitu)