Predictive energy control for grid-connected industrial PV-battery systems using GEP-ANFIS

• Published in: e-Prime Vol. 9; p. 100647
• Main Authors: Bakare, Mutiu Shola, Abdulkarim, Abubakar, Shuaibu, Aliyu Nuhu, Muhamad, Mundu Mustafa
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.09.2024; Elsevier
• Subjects: ANFIS; Energy management; Gene expression programming; Predictive energy control; Solar PV-battery systems; Time of use pricing; ANN; CNN; GTO; MPPT; PV; RMPC; SMEs; UEDCL; EHOA; HRES; ANFIS; Gene expression programming; MTSO; MAPE; EMS; RMSE; ZOA; LCOE; MAD; GEP; ERA; BEMS; Time of use pricing; Solar PV-battery systems; TOU; Energy management; Predictive energy control
• ISSN: 2772-6711; 2772-6711
• DOI: 10.1016/j.prime.2024.100647

•Investigated the feasibility of implementing optimization-based Time of Use (TOU) Demand-Side Management (DSM) strategies in Uganda's industrial sector, utilizing grid-connected solar and battery energy sources to mitigate peak electricity demands and reduce costs.•Developed a hybrid Gene Expression Programming (GEP) and Adaptive Network-Based Fuzzy Inference Systems (ANFIS) model, optimizing energy management using historical load patterns, grid data, time-of-day information, energy prices, and solar PV/battery inputs.•Demonstrated the GEP-ANFIS model's superiority in predictive energy control, achieving a lower mean absolute percentage error (MAPE) of 2.24 %, a mean absolute deviation (MAD) of 0.13 %, and a root mean square error (RMSE) of 0.18 % in short-term load demand prediction.•Achieved significant energy cost savings with the GEP-ANFIS model, reducing costs by 6.7 % on average compared to traditional approaches, highlighting the potential of hybrid techniques for cost-effective energy management in industrial settings. Rising energy costs in Uganda's industrial sector have significantly increased manufacturing expenses and led to the closure of potential enterprises, underscoring the urgent need for efficient energy management solutions. The study presents an optimization-based predictive energy control model that integrates Gene Expression Programming (GEP) and Adaptive Network-based Fuzzy Inference Systems (ANFIS) for grid-connected solar PV-battery systems. The hybrid GEP-ANFIS model leverages GEP's strength in complex pattern recognition and ANFIS's adaptive learning capability to manage nonlinear data, resulting in more accurate and reliable energy predictions. The model utilizes historical load patterns, grid data, and solar PV/battery inputs to forecast load demand and solar power generation, optimizing energy usage based on Time of Use (TOU) pricing. The approach addresses the limitations of existing energy management techniques by enhancing predictive accuracy and ensuring seamless integration with industrial applications. The present work demonstrates that the proposed GEP-ANFIS double diode model consistently outperforms its counterparts, achieving a mean absolute percentage error (MAPE) of 7.25 %, a mean absolute deviation (MAD) of 2.95 %, and a root mean square error (RMSE) of 8.42 %. Moreover, the model exhibits superior accuracy in predicting short-term load demand, with a MAPE of 2.24 %, a MAD of 0.13 %, and an RMSE of 0.18 %. Additionally, the model results in an average energy cost reduction of 6.7 % for hybrid grid-connected solar PV/battery configurations, highlighting its economic benefits. The study's key contributions include the creation of a novel hybrid predictive model, significant cost savings, improved load prediction accuracy, and the integration of renewable energy sources into industrial energy management. The model's inferences can effectively address the challenges of rising energy costs, providing a robust framework for sustainable and efficient energy management in industrial settings.