A Novel Hybrid Machine Learning-IoT Framework for Optimizing Solar Energy Efficiency in Arid Regions: A Case Study of Sub-Saharan Africa DOI

Benjamin Nyabera Kerama

Published: May 15, 2025

The efficient harnessing of solar energy in arid regions is critical for closing the electricity access gap Sub- Saharan Africa, yet installations routinely underperform due to soiling, extreme temperatures, and lack adaptive control. We introduce a novel hybrid Machine Learning–IoT framework that unifies real-time environmental electrical sensing, deep-learning prediction power output fault risk, reinforcement-learning–based adjustment panel tilt maintenance scheduling. cast as constrained optimization problem balancing yield, cost, reliability, employs multi-stage ML pipeline—combining LSTM XGBoost generation forecasting CNN-based classifier anomaly detection—together with Deep Q-Network controller. validate our approach via year-long simulation 100 kW off-grid PV array Northern Kenya. Compared fixed- tilt, quarterly-cleaning baseline, method achieves 20.8 % increase annual 35.5 reduction downtime, while respecting practical bounds on angles service frequency maintaining fault-risk below prescribed threshold. These results demonstrate end-to-end integration IoT machine learning, optimal control can substantially enhance performance, cost-effectiveness, reliability deployments harsh, resource-constrained environments.

Language: Английский

Internet of Things Applications for Energy Management in Buildings Using Artificial Intelligence—A Case Study DOI Creative Commons
Izabela Rojek, Dariusz Mikołajewski, Adam Mroziński

et al.

Energies, Journal Year: 2025, Volume and Issue: 18(7), P. 1706 - 1706

Published: March 28, 2025

IoT applications for building energy management, enhanced by artificial intelligence (AI), have the potential to transform how is consumed, monitored, and optimized, especially in distributed systems. By using sensors smart meters, buildings can collect real-time data on usage patterns, occupancy, temperature, lighting conditions.AI algorithms then analyze this identify inefficiencies, predict demand, suggest or automate adjustments optimize use. Integrating renewable sources, such as solar panels wind turbines, into systems uses IoT-based monitoring ensure maximum efficiency generation These also enable dynamic pricing load balancing, allowing participate grids storing selling excess energy.AI-based predictive maintenance ensures that systems, inverters batteries, operate efficiently, minimizing downtime. The case studies show AI are driving sustainable development reducing consumption carbon footprints residential, commercial, industrial buildings. Blockchain further secure transactions increasing trust, sustainability, scalability. combination of IoT, AI, sources line with global trends, promoting decentralized greener study highlights adopting management offers not only environmental benefits but economic benefits, cost savings independence. best achieved accuracy was 0.8179 (RMSE 0.01). overall effectiveness rating 9/10; thus, AI-based solutions a feasible, cost-effective, approach office management.

Language: Английский

Citations

0
Integration of Renewable Energy Systems Into Smart Cities DOI
S. Ida Evangeline

IGI Global eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 387 - 404

Published: April 17, 2025

A necessary step in creating a sustainable, secure and resilient smart cities is the integration of renewable energy systems. The expansion increasing demand make adoption technologies, solar, wind geothermal power, vital to reduce carbon footprint create clean highly efficient use energy. Digital innovations such as Internet Things (IoT), artificial intelligence (AI) blockchain bring about framework for city usage resources management optimization. This chapter discusses technological, economic, policy aspects incorporating into urban context from perspective grids, storage options, decentralized power generation, intelligent However, there are tremendous benefits energy, conservation environment, economic progress, well augmentation community's resilience. Further research advancement infrastructure be able overcome technical challenges instance issues intermittency, limitations, complexities grid integration. Limited funds test financial constraints, especially, high initial cost projects necessitate innovative funding mechanism supportive frameworks. There also social equity concerns that must managed provide utilization affordable all communities, regardless income.

Language: Английский

Citations

0
A Novel Hybrid Machine Learning-IoT Framework for Optimizing Solar Energy Efficiency in Arid Regions: A Case Study of Sub-Saharan Africa DOI

Benjamin Nyabera Kerama

Published: May 15, 2025

The efficient harnessing of solar energy in arid regions is critical for closing the electricity access gap Sub- Saharan Africa, yet installations routinely underperform due to soiling, extreme temperatures, and lack adaptive control. We introduce a novel hybrid Machine Learning–IoT framework that unifies real-time environmental electrical sensing, deep-learning prediction power output fault risk, reinforcement-learning–based adjustment panel tilt maintenance scheduling. cast as constrained optimization problem balancing yield, cost, reliability, employs multi-stage ML pipeline—combining LSTM XGBoost generation forecasting CNN-based classifier anomaly detection—together with Deep Q-Network controller. validate our approach via year-long simulation 100 kW off-grid PV array Northern Kenya. Compared fixed- tilt, quarterly-cleaning baseline, method achieves 20.8 % increase annual 35.5 reduction downtime, while respecting practical bounds on angles service frequency maintaining fault-risk below prescribed threshold. These results demonstrate end-to-end integration IoT machine learning, optimal control can substantially enhance performance, cost-effectiveness, reliability deployments harsh, resource-constrained environments.

Language: Английский

Citations

0