It is essential for the electricity sector to analyze and determine the distribution capacity throughput and apply new methods aimed at increasing the capacity of the transmission system. Consequently, the transition to modern electricity networks is two-sided, i.e., involving technological and social modifications. The demand response (DR) redistributes consumption away from peak times when grid load and costs are the highest. It incentivizes customers to use electricity when supply is high and inexpensive due to various market mechanisms. The present DR policy proposals stress the importance of fostering behavioral change through competitive pricing and customer participation in reducing carbon emissions and implementing smart energy solutions (including monitoring tools, such as smart meters and applications). The internet of things (IoT) has been applied to ensure adaptive monitoring of energy consumption and cost-effective and adequate demand-side management (DSM). The article is based on the research of the most recent sources of DR implementation methods applied at the power distribution level. It explains the main concepts, classifications, and entities implementing DSM programs, and suggests new visions and prospects for DSM and DR. Moreover, it discusses the application of blockchain technology potential for the internet of energy.

The energy industry is experiencing a major change due to fast progress in artificial intelligence (AI). Startup companies in this revolution use AI technologies like Machine Learning (ML), predictive analytics, and optimization algorithms to improve energy efficiency, optimize grid management, and incorporate renewable energy sources. AI-powered solutions allow for a more accurate prediction of demand, immediate monitoring, and automated decision-making processes, significantly enhancing operational efficiency and sustainability. Through promoting a more effective energy system, these advancements play a vital role in the worldwide battle against climate change and carbon dioxide emissions. Adding to the progress of AI, quantum computing (QC) shows great potential despite being a nascent area. The collaboration of AI and QC is poised to transform the energy industry by offering unmatched computational capabilities. This blend of technologies can tackle intricate energy obstacles like enhancing power grids and enhancing battery storage, which traditional computers cannot currently handle. Combining QC with AI speeds up innovation, providing advanced solutions that improve the resilience and efficiency of energy networks. This paper discusses the latest advancements, possible effects, and upcoming paths of new companies leading in AI and QC innovations within the energy industry. Their joint responsibility is highlighted in advancing a sustainable and intelligent energy future, as well as tackling crucial environmental issues and lessening the impact of climate change.

The extreme volatility of European energy markets in 2022 has exposed the limitations of conventional forecasting models, necessitating more robust architectures capable of handling non-linear price shocks. This study proposes a novel multi-parallel hybrid forecasting framework that integrates seven heterogeneous neural networks to predict day-ahead electricity prices. The architecture employs a hierarchical approach where six parallel base models (NN1–NN6) feed into a meta-network (NN7) to generate baseline forecasts. To further enhance predictive fidelity, these results undergo a calibration stage using probabilistic error distribution analysis to produce final probability-adjusted forecasts. The model was validated using the Lithuanian electricity market during the highly volatile period of 2020–2022. Empirical results demonstrate a clear “stacking effect,” where the incremental integration of base networks consistently reduces forecasting residuals. The final probability-adjusted configuration achieved a notable nMAE of 1.57% and a sMAPE of 34.25%, significantly outperforming baseline ensemble outputs and state-of-the-art benchmarks reported in recent literature. Specifically, the probability-based refinement proved highly effective in mitigating systematic biases during nighttime and early morning hours, confirming the model’s capacity to maintain accuracy under extreme market stress.