Renewable Energy News and Research

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Optimizing Smart Home Appliance Scheduling for Economic and Energy EfficiencyTop of Form

Researchers propose a two-phase optimization framework for smart home appliance scheduling. Integrating metaheuristics like Binary Particle Swarm Optimization and Self-Organizing Hierarchical PSO, the approach balances electricity expenses and user comfort, showcasing substantial reductions in costs and peak loads. The innovative method holds promise for widespread application, offering insights for sustainable energy management in diverse sectors beyond residential homes.

18 Dec 2023

Integrating IoT Technology for Efficient Load Management in Residential Distribution Systems

Researchers introduce an innovative framework that integrates Internet of Things (IoT) technology, particularly IoT windows and occupant behavior, into residential distribution systems to enhance load management and conserve energy. The study highlights the potential of IoT-based load optimization, discusses its impact on distribution operations and underscores the importance of coordination for aligning IoT-enabled household energy consumption with rooftop photovoltaic generation in intelligent grid systems for improved sustainability.

2 Nov 2023

Smart Greenhouse IoT System: A Breakthrough in Crop Cultivation

This article outlines the development and implementation of a smart greenhouse system using IoT technologies. This comprehensive solution integrates automation, precise growth parameter fine-tuning, and user-friendly Android mobile interfaces, offering real-time control and improved crop management in Brassica Juncea cultivation.

1 Nov 2023

AI-operated Satellites to Boost Global Connectivity and Improve Land Use

Tenchijin, a Japanese startup, is utilizing deep learning and satellite data to address issues with satellite internet, particularly the impact of weather on ground stations. Their AI system accurately predicts suitable ground stations, providing more reliable internet connectivity, and their COMPASS service has applications in renewable energy, agriculture, and city planning by optimizing land use decisions using a variety of data sources.

23 Oct 2023

Harnessing AI for Environmental Solutions: Opportunities and Challenges

The use of Artificial Intelligence (AI) in environmental science is on the rise, offering efficient ways to analyze complex data and address ecological concerns. However, the energy consumption and carbon emissions associated with AI models are concerns that need mitigation. Collaboration between environmental and AI experts is essential to maximize AI's potential in addressing environmental challenges while ensuring ethical and sustainable practices.

16 Oct 2023

Enhancing Short-Term Solar Energy Forecasting with Deep Learning Models

Researchers used a combination of machine learning and deep learning models, including Bi-LSTM variants, to improve short-term solar energy predictions based on climatic factors in Amherst. Deep learning models consistently outperformed traditional machine learning techniques, highlighting their potential to enhance the accuracy and reliability of solar energy forecasts, crucial for efficient renewable energy utilization.

6 Oct 2023

Unlocking Biomass Energy Potential: ML Model for HHV Estimation

Researchers have developed a robust machine learning model, specifically a multilayer perceptron neural network (MLPNN), to accurately estimate the higher heating value (HHV) of biomass. By combining feature selection techniques with ML, this study offers superior accuracy in predicting HHV, contributing to advancements in renewable energy from agricultural byproducts.

29 Sep 2023

Harnessing Machine Learning for Advancing Offshore Wind Energy

Researchers have conducted a comprehensive review of the offshore wind energy industry, emphasizing the role of machine learning (ML) and artificial intelligence (AI) in addressing challenges related to turbine size, efficiency, environmental impact, and deep-water deployment. ML applications include climate forecasting, environmental impact assessment, wind farm optimization, and more.

26 Sep 2023

Revolutionizing Energy Planning: AI-Powered Oil Demand Forecasting

Researchers have harnessed the power of artificial intelligence to forecast oil demand in both exporting and importing nations, providing policymakers and energy stakeholders with precise tools for navigating the complex global oil market landscape. Their study compared AI techniques with traditional statistical models, revealing the superiority of AI in terms of prediction accuracy and stability.

14 Sep 2023

From Classrooms to Smart Universities: AI’s role in Transforming Higher Education

Researchers discuss how artificial intelligence (AI) is reshaping higher education. The integration of AI in universities, known as smart universities, enhances efficiency, personalization, and student experiences. However, challenges such as job displacement and ethical considerations require careful consideration as AI's transformative potential in education unfolds.

31 Aug 2023

Optimizing Electric Vehicle Operation in Multimicrogrids through Coordinated Control

Researchers introduce an innovative approach to optimize the operation of electric vehicles (EVs) within multimicrogrids (MMGs) in a paper published in the journal Sustainability. Their novel method involves a four-level control hierarchy, encompassing microsource controllers, unit controllers, microgrid controllers, and a top-level multimicrogrid controller.

23 Aug 2023

Advancing Solid Biofuels Classification in IoT-driven Smart Cities

Researchers highlight the role of solid biofuels and IoT technologies in smart city development. They introduce an IoT-based method, Solid Biofuel Classification using Sailfish Optimizer Hybrid Deep Learning (SBFC-SFOHDL), which leverages deep learning and optimization techniques for accurate biofuel classification.

21 Aug 2023

Affordable Wireless Energy Meter for Accurate Power Quality Monitoring in Smart Grids

Researchers introduce a cost-effective wireless energy meter employing the ESP32 microcontroller for power quality monitoring in smart grid applications. By integrating sentiment analysis using Word2vec and LSTM, the model efficiently captures emotional influences on the global economy, leading to improved accuracy and reduced energy consumption.

19 Aug 2023

Revolutionizing Renewable Energy Forecasting with Graph Patch Informer

Researchers introduce the Graph Patch Informer (GPI) as a novel approach for accurate renewable energy forecasting (REF). Combining self-attention, graph attention networks (GATs), and self-supervised pre-training, GPI outperforms existing models and addresses challenges in long-term modeling, missing data, and spatial correlations. The model's effectiveness is demonstrated across various REF tasks, offering a promising solution for stable power systems and advancing renewable energy integration.

16 Aug 2023

Enhancing Eco-Friendly Air Pollutant Control in Coal-Powered Plants Using AI

Researchers propose an innovative approach to enhance the air pollutant removal system in coal-fired power plants. By integrating AI-driven models, Monte Carlo simulations, and multi-criteria decision-making, this study offers an optimized configuration for sustainable pollutant control. The proposed setup showcases promising outcomes, including economic efficiency, environmental quality improvement, and enhanced reliability, underscoring the potential for transforming pollutant management in the energy industry.

9 Aug 2023

Green AI: Transforming Energy Forecasting for a Sustainable Future

Researchers delve into the world of Green AI, a promising technology that combines artificial intelligence with sustainability practices to address energy forecasting and management challenges. The article explores applications in green energy load forecasting, power consumption prediction, and electricity price forecasting, highlighting the potential of Green AI to optimize energy distribution, promote renewable energy sources, and foster a greener and more sustainable future.

2 Aug 2023

Boosting Electric Vehicle Performance: Hybrid Deep Neural Network Unleashes State of Charge Estimation

Researchers introduce a hybrid approach combining a multi-layer deep neural network (DNN) and the mountain gazelle optimizer (MGO) to accurately estimate the state of charge (SoC) in electric vehicle (EV) batteries. The proposed technique outperforms existing methods, offering superior accuracy and faster convergence, with the potential to optimize EV operations and extend battery lifespan.

13 Jul 2023