Search

Energy management systems allow the Smart Grids industry to track, improve, and regulate energy use. Particularly, demand-side management is regarded as a crucial component of the entire Smart Grids system. Therefore, by aligning utility offers with customer demand, anticipating future energy demands is essential for regulating consumption. An updated examination of several forecasting techniques for projecting energy short-term load forecasts is provided in this article. Each class of algorithms, including statistical techniques, Machine Learning, Deep Learning, and hybrid combinations, are comparatively evaluated and critically analyzed, based on three real consumption datasets from Spain, Germany, and the United States of America. To increase the size of tiny training datasets, this paper also proposes a data augmentation technique based on Generative Adversarial Networks. The results show that the Deep Learning-hybrid model is more accurate than traditional statistical methods and basic Machine Learning procedures. In the same direction, it is demonstrated that more comprehensive datasets assisted by complementary data, such as energy generation and weather, may significantly boost the accuracy of the models. Additionally, it is also demonstrated that Generative Adversarial Networks-based data augmentation may greatly improve algorithm accuracy. [ABSTRACT FROM AUTHOR]

Combined Heat and Power (CHP) has been identified by the EU as a powerful resource capable of making substantial contributions to energy savings and reducing GHG emissions. Spain's effort to promote CHP has been prolific since the 1980s. In this regard, there have been various Laws, Royal Decrees (RDs) and European Union (EU) Directives addressed to reach the national objectives set for the CHP sector. Despite these attempts, the evolution and growth of installed CHP capacity has been irregular, compared to other technologies. Likewise, the academic treatment of the Spanish CHP evolution has not deserved the same attention as other technologies such as wind, photovoltaic and thermal solar systems. As a result, this article is aimed at providing a comprehensive overview of the regulatory frameworks applied to the Spanish CHP sector and analysing the reasons behind the variable evolution of the installed CHP capacity. The study covers the legislative context from 1980 to 2020, describing the evolution during both the pre-liberalization and liberalization periods, highlighting the modifications in economic policies that affected self-producers and the so-called Special Regime (SR) for CHP, and examining the challenges faced during the cost containment measures that followed. The manuscript finds and explains the connection between the regulatory framework and the evolution of installed CHP capacity in Spain. Likewise, the connection between the industrial situation and the promotion of CHP, as well as the influence of the Spanish Electricity Sector (SES)'s liberalization on the CHP sector are also pointed out. The paper intends to provide valuable insights for CHP experts and policymakers by showcasing the importance of aligning regulatory measures with the objectives of energy efficiency. It also serves as a reference for countries in various stages of promoting CHP, and provides evidence for the importance of stable energy-policy control mechanisms. [ABSTRACT FROM AUTHOR]