Advancing the Energy Internet: Innovations and Solutions for a Sustainable Energy Future

Topic Information

Dear Colleagues,

The Energy Internet represents a transformative paradigm integrating advanced power systems, distributed renewable energy, and digital technologies to achieve efficient, resilient, and sustainable energy management. As global decarbonization efforts intensify, the Energy Internet’s core components—including smart grid situational awareness, renewable integration optimization, AI-driven microgrid control, and cloud-based big data analytics—are critical to addressing challenges in grid stability, energy equity, and carbon neutrality. Notably, the convergence of artificial intelligence, edge computing, and IoT technologies with power systems has unlocked unprecedented opportunities for real-time decision-making, predictive maintenance, and demand-side management. However, the complexity of multi-energy synergies, cyber-physical security risks, and the volatility of renewable generation necessitate innovative solutions in data-driven modeling, adaptive control, and scalable infrastructure design. This Topic invites cutting-edge research on theoretical advancements, empirical case studies, and technological innovations to propel the Energy Internet toward scalability and interoperability.

Contributions may address, but are not limited to, the following areas:

• Situational awareness and dynamic stability analysis for smart distribution grids;
• Optimization control strategies for high-penetration renewable energy integration;
• AI/ML applications in microgrid energy management and fault diagnosis;
• Blockchain-enabled peer-to-peer energy trading and decentralized governance;
• Big data analytics for load forecasting, asset management, and grid anomaly detection;
• Cyber security and resilience in cyber–physical power systems;
• Edge-cloud collaborative computing for distributed energy resource coordination;
• Standardization and policy frameworks for Energy Internet deployment;
• Socio-economic impacts and business models for transactive energy systems;
• Digital twins and virtual power plants for grid flexibility enhancement.

Dr. Leijiao Ge
Dr. Dou An
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Electricity electricity	1.8	5.1	2020	26 Days	CHF 1200	Submit
Electronics electronics	2.6	6.1	2012	16.8 Days	CHF 2400	Submit
Energies energies	3.2	7.3	2008	16.2 Days	CHF 2600	Submit
Future Internet futureinternet	3.6	8.3	2009	17 Days	CHF 1600	Submit
IoT IoT	2.8	8.7	2020	25.7 Days	CHF 1400	Submit
Sensors sensors	3.5	8.2	2001	19.7 Days	CHF 2600	Submit

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Published Papers (1 paper)

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All Electricity Electronics Energies Future Internet IoT Sensors

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23 pages, 2258 KB  

Open AccessArticle

A High-Precision Short-Term Photovoltaic Power Forecasting Model Based on Multivariate Variational Mode Decomposition and Gated Recurrent Unit-Attention with Crested Porcupine Optimizer-Enhanced Vector Weighted Average Algorithm

by Jinxiang Pian and Xianliang Chen

Sensors 2025, 25(19), 5977; https://doi.org/10.3390/s25195977 - 26 Sep 2025

Abstract

The increasing reliance on renewable energy sources, such as photovoltaic (PV) systems, is pivotal for achieving sustainable development and addressing global energy challenges. However, short-term power forecasting for distributed PV systems often faces accuracy limitations, hindering their efficient grid integration. To address this, [...] Read more.

The increasing reliance on renewable energy sources, such as photovoltaic (PV) systems, is pivotal for achieving sustainable development and addressing global energy challenges. However, short-term power forecasting for distributed PV systems often faces accuracy limitations, hindering their efficient grid integration. To address this, a novel hybrid prediction model is proposed, combining multivariate variational mode decomposition (MVMD) with a gated recurrent unit (GRU) network, an attention mechanism (ATT), and an enhanced vector weighted average algorithm (cINFO). The MVMD first decomposes historical data to reduce volatility. The INFO algorithm is then improved by integrating the crested porcupine optimizer (CPO), forming the cINFO algorithm to optimize GRU-ATT hyperparameters. An attention mechanism is incorporated to accentuate key influencing factors. The model was evaluated using the DKASC Alice Springs dataset. Results demonstrate high predictive accuracy, with mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²) values of 0.0249, 0.0693, and 99.79%, respectively, under sunny conditions, significantly outperforming benchmark models. This confirms the model’s feasibility and superiority for short-term PV power forecasting. Full article

(This article belongs to the Topic Advancing the Energy Internet: Innovations and Solutions for a Sustainable Energy Future)

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