Authors:
D. Suraj, Najib Sulthan, R. Balaji, R. Swathi, Charlotte Roberts, B. Vaidianathan

Addresses:
Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, Tamil Nadu, India. Australian Graduate School of Engineering (AGSE), University of New South Wales, Sydney, Australia. Department of Electronics and Communication Engineering, Dhaanish Ahmed College of Engineering, Chennai, Tamil Nadu, India. surajdoraiswamy@gmail.com, najib4cr7@gmail.com, balaji200219@gmail.com, swathir2@srmist.edu.in, charlotteroberts.gs@gmail.com, vaidianathan@dhaanishcollege.in.

Abstract:

Building energy demand and energy system supply are increasingly balanced by energy storage and short-term DSM. This is necessary due to fluctuating renewable energy supplies and rising building power use. Worldwide, structures utilize tons of energy. Greenhouse gas emissions and operational expenses decrease with construction energy efficiency. Forecasting and optimization algorithms can solve challenges, including supply chains (inventory optimization), traffic, and sustainable energy system battery/load/production scheduling for carbon-free energy generation. We often solve optimization problems that need forecasting due to uncertain future values. Predicting and optimizing are challenging; therefore, little research has been done. Our method uses building energy modeling professionals' data to forecast neighboring building types for new or unknown building types. After training, we utilize the models to estimate energy usage for the k-closest building types and combine the predictions using a weighted average. We used time-series decomposition to detect uncertainty and a hybrid model to close this gap: The concepts encode static features and predictable patterns in time-series simulation results. The model learns latent performance differences and calibrates output using outcomes and history records. Historical data predicts public building energy ARIMA use. Our method covers data processing, training, validation, and forecasting. We measured our method. Mixed integer linear optimization and ARIMA projected most accurately.

Keywords: Gradient Boosting (GB); Combined Cycle Power Plant; Auto-Regressive Integrated Moving Average (ARIMA); Seasonal Autoregressive Integrated Moving Average with Exogenous Factors (SARIMAX); Long Short-Term Memory.

Received on: 29/10/2023, Revised on: 02/01/2024, Accepted on: 03/03/2024, Published on: 09/06/2024

DOI: 10.69888/FTSES.2024.000187

FMDB Transactions on Sustainable Energy Sequence, 2024 Vol. 2 No. 1, Pages: 1-11

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