Harmonic distortion is one of the disturbances that most affects the quality of the electrical system. The widespread use of power electronic systems, especially power converters, has increased harmonic and interharmonic emission in a wide range of frequencies. Therefore, there are new needs in the measurement of harmonic distortion in modern electrical systems, such as measurement in the supra-harmonic range (>2 kHz) and the measurement of interharmonics. The International Electrotechnical Commission (IEC) standards define new total harmonic distortion (THD) rates based on the concept of frequency groupings. However, the rates defined in the IEC standards have shortcomings when measuring signals such as those present in the outputs of power systems with abundant interharmonic content and presence of components in the supra-harmonic range. Therefore, in this work, a comparison is made between the different THD factors currently defined, both in the literature and in the standards, to show which of them are the most suitable for assessing harmonic and interharmonic contamination in power system signals such as those present at the output of inverters.

Buildings are among the largest energy consumers in the world. As new technologies have been developed, great advances have been made in buildings, turning conventional buildings into smart buildings. These smart buildings have allowed for greater supervision and control of the energy resources within the buildings, taking steps to energy management strategies to achieve significant energy savings. The forecast of energy consumption in buildings has been a very important element in these energy strategies since it allows adjusting the operation of buildings so that energy can be used more efficiently. This paper presents a review of energy consumption forecasting in smart buildings for improving energy efficiency. Different forecasting methods are studied in nonresidential and residential buildings. Following this, the literature is analyzed in terms of forecasting objectives, input variables, forecasting methods and prediction horizon. In conclusion, the paper examines future challenges for building energy consumption forecasting.

Background: Forecasting renewable energy generation is crucial for improving the efficiency and reliability of power systems that integrate wind, solar, and other renewable sources. These energy sources are inherently variable, depending on changing weather patterns, which makes accurate forecasting a complex task. The ability to predict renewable energy production with high accuracy can help grid operators optimize energy storage, reduce the reliance on fossil fuels, and ensure grid stability. Forecasting methods vary significantly, ranging from physical models that rely on weather data, to statistical models and advanced machine learning techniques. Each method has its own strengths and limitations, and the choice of approach often depends on the specific requirements, such as time horizon, data availability, and computational resources. Methods: Various forecasting techniques are employed to predict renewable energy generation, including physical, statistical, machine learning (ML), and hybrid models. Physical models, such as Numerical Weather Prediction (NWP), are commonly used for short-term forecasting based on weather data. Statistical methods, such as ARIMA and SARIMA, are often applied to time series data in stable environments. Machine learning models, including neural networks (ANN, LSTM) and decision trees (XGBoost, LightGBM), are employed to model complex, non-linear relationships in large datasets. Hybrid models, which combine physical and machine learning methods, have also emerged to enhance predictive accuracy and adaptability. Results: The reviewed studies highlight the strengths and limitations of each approach. Physical models, such as NWP, perform well in short-term predictions but are limited in long-term forecasting and require high-quality input data. Statistical methods, including ARIMA and SARIMA, are effective in stable environments but struggle with non-linear patterns and abrupt changes. Machine learning models, especially neural networks (ANN, LSTM), show great promise in capturing non-linear relationships and long-term trends in renewable energy generation, but they require large amounts of data and are computationally intensive. Hybrid models have demonstrated superior accuracy by combining the strengths of physical and machine learning approaches. Conclusions: The integration of hybrid models offers a promising direction for improving renewable energy forecasting. While these models provide high accuracy and adaptability, they also come with significant challenges, such as high computational costs and the complexity of model integration. Future research should focus on optimizing hybrid methods for balancing accuracy and computational efficiency, exploring lightweight machine learning techniques, and enhancing explainable AI (XAI) approaches to increase model transparency. Additionally, the development of regionally adaptable models and standardized datasets will improve model reproducibility and accuracy across different geographic locations and climatic conditions.

Power quality (PQ) has received the attention of several research groups due to the impact of PQ disturbances (PQDs) and how they affect the operation of the electrical equipment connected to the grid, especially in industrial and healthcare facilities. The monitoring and analysis of PQD are generally performed with specialized measuring equipment, such as power analyzers, based on the standards. However, this equipment is not suited to perform continuous monitoring and classification of PQD, and it cannot be configured to perform further analysis of the monitored signal. Smart sensors, on the other hand, can provide the functionality that the standard equipment cannot, by integrating several signal processing modules that can be reconfigured using a reconfigurable technology, such as field programmable gate arrays (FPGAs). This paper presents the development of an FPGA-based smart sensor that integrates the processing cores of higher order statistics (HOS) to provide a signal analysis aimed to detect and quantify PQD on electrical installations and an artificial neural network to classify the PQD. Experimentation is performed on the electrical installation in hospital facilities. Results from the HOS processing of electrical signals show that these processing methodologies are suitable for the quantification and classification of PQD on electrical installations.

Buildings are currently among the largest consumers of electrical energy with considerable increases in CO₂ emissions in recent years. Although there have been notable advances in energy efficiency, buildings still have great untapped savings potential. Within demand-side management, some tools have helped improve electricity consumption, such as energy forecast models. However, because most forecasting models are not focused on updating based on the changing nature of buildings, they do not help exploit the savings potential of buildings. Considering the aforementioned, the objective of this article is to analyze the integration of methods that can help forecasting models to better adapt to the changes that occur in the behavior of buildings, ensuring that these can be used as tools to enhance savings in buildings. For this study, active and passive change detection methods were considered to be integrators in the decision tree and deep learning models. The results show that constant retraining for the decision tree models, integrating change detection methods, helped them to better adapt to changes in the whole building’s electrical consumption. However, for deep learning models, this was not the case, as constant retraining with small volumes of data only worsened their performance. These results may lead to the option of using tree decision models in buildings where electricity consumption is constantly changing.