Future-Ready Smart Grids Harnessing Predictive Analytics and Optimization for Sustainable Energy

TY - GEN

T1 - Future-Ready Smart Grids Harnessing Predictive Analytics and Optimization for Sustainable Energy

AU - Olughu, Daniel Sunday

AU - Cai, Dongsheng

AU - Bamisile, Olusola

AU - Ukwuoma, Chiagoziem C.

AU - Alfred, Olugbenle Olatomide

AU - Olasehinde, Daniel O.

N1 - Publisher Copyright: © 2026 SPIE.

PY - 2026/2/2

Y1 - 2026/2/2

N2 - Due to increasing energy demands around the world and rising environmental concerns, there is a call for the addition of renewable resources to the grid. In general, smart grids allow for real-time monitoring of the power system, but do not have the ability to forecast and predict generation from variable solar or wind generation and effectively allocate the optimal energy mix. Literature tends to focus on either machine learning (ML) forecasting or linear programming (LP) allocation, but lacks an integrated article. This paper integrates ML models (Random Forest Regressor (RF), Support Vector Regression (SVR), and Multilayer Perceptron (MLP)) for forecasting unknown generation into LP for allocation. The RF model outperformed the others, achieving an RMSE of 0.0110 and an R2 of 0.9998. The LP optimization successfully allocated energy to meet demand while maximizing the renewable use. Results from the tests indicated that the optimized grid could meet approximately 100% of demand with renewables in the best conditions, making significant advances to sustainability despite limitations in computing power.

AB - Due to increasing energy demands around the world and rising environmental concerns, there is a call for the addition of renewable resources to the grid. In general, smart grids allow for real-time monitoring of the power system, but do not have the ability to forecast and predict generation from variable solar or wind generation and effectively allocate the optimal energy mix. Literature tends to focus on either machine learning (ML) forecasting or linear programming (LP) allocation, but lacks an integrated article. This paper integrates ML models (Random Forest Regressor (RF), Support Vector Regression (SVR), and Multilayer Perceptron (MLP)) for forecasting unknown generation into LP for allocation. The RF model outperformed the others, achieving an RMSE of 0.0110 and an R2 of 0.9998. The LP optimization successfully allocated energy to meet demand while maximizing the renewable use. Results from the tests indicated that the optimized grid could meet approximately 100% of demand with renewables in the best conditions, making significant advances to sustainability despite limitations in computing power.

KW - Energy optimization

KW - IoT

KW - Linear programming

KW - Machine learning

KW - Renewable energy

KW - Smart grid

UR - https://www.scopus.com/pages/publications/105030649046

U2 - 10.1117/12.3098746

DO - 10.1117/12.3098746

M3 - Conference contribution

AN - SCOPUS:105030649046

T3 - Proceedings of SPIE - The International Society for Optical Engineering

BT - International Conference on Computer Vision and Machine Learning, CVML 2025

A2 - Tistarelli, Massimo

A2 - Fan, Rui

PB - Society of Photo-optical Instrumentation Engineers

T2 - International Conference on Computer Vision and Machine Learning, CVML 2025

Y2 - 24 October 2025 through 26 October 2025

ER -