Short-term load forecasting (STLF) plays a vital role in ensuring the safe, efficient, and economical operation of power systems. Accurate load forecasting provides numerous benefits for power suppliers, such as cost reduction, increased reliability, and informed decision-making. However, STLF is a complex task due to various factors, including non-linear trends, multiple seasonality, variable variance, and significant random interruptions in electricity demand time series. To address these challenges, advanced techniques and models are required. This study focuses on the development of an efficient short-term power load forecasting model using the random forest (RF) algorithm. RF combines regression trees through bagging and random subspace techniques to improve prediction accuracy and reduce model variability. The algorithm constructs a forest of trees using bootstrap samples and selects random feature subsets at each node to enhance diversity. Hyperparameters such as the number of trees, minimum sample leaf size, and maximum features for each split are tuned to optimize forecasting results. The proposed model was tested using historical hourly load data from four transformer substations supplying different campus areas of the University of Beira Interior, Portugal. The training data were from January 2018 to December 2021, while the data from 2022 were used for testing. The results demonstrate the effectiveness of the RF model in forecasting short-term hourly and one day ahead load and its potential to enhance decision-making processes in smart grid operations.

Food waste occurs from harvesting to consumption. Applying procedures and technologies, changing attitudes, and promoting awareness have positive social, economic, and environmental impacts that can contribute to reducing food waste. The paper presents a decision support system (DSS) to predict the quality evolution of fruits and vegetables, particularly of peaches, and estimate its commercialization period at the highest overall perceived quality by consumers, thus contributing to reducing food waste. The Fuzzy Logic DSS predicts the evolution of the physical-chemical parameters of peaches (hardness, soluble solids content, and acidity) depending on the cultivar (Royal Summer and Royal Time), storage time, and temperature. As the range of the values of these physical-chemical parameters of peaches that consumers perceive to be at their highest quality are known, the DSS predicts the marketable period in days. Case studies were developed to analyze the influence of each physical-chemical parameter on the commercialization days (number and time to start). It is concluded that temperature is the most important parameter for fruit conservation. A low value of conservation temperature allows for the significant extension of the time that peaches can be sold at the highest quality. Hardness is used to determine the harvest date since it is an index of fruit ripeness. The same conclusion is obtained for the influence of the soluble solids content. The influence of acidity on marketable days is less than the other physical-chemical parameters. This DSS helps retailers to sell their peaches at the highest quality with benefits for all parties. It also helps in the decision-making concerning the actions to take when fruits are reaching the end of their highest quality by predicting the range of the commercialization days. This formulation can be extended to other fruits and vegetables and in the last instance contribute to the reduction of food loss and waste, consequently promoting social, economic, and environmental aspects of our daily life.

The increasing importance of photovoltaic (PV) systems in the context of the energy transition, together with the need to improve their efficiency, has driven the adoption and development of intelligent and advanced maximum power point tracking (MPPT) techniques. Among these approaches, the Particle Swarm Optimization (PSO) algorithm stands out due to its simplicity, ease of implementation, low number of control parameters, robustness, and fast convergence capability, making it widely applied in modern MPPT systems. However, the performance of PSO in MPPT applications depends on the appropriate selection of both algorithm control parameters and implementation/configurations parameters. The control parameters include the cognitive (C₁) and social (C₂) learning factors, as well as the inertia factor (w), which directly influence swarm dynamics and the balance between exploration and exploitation mechanisms, that is, between global and local search. On the other hand, configuration parameters such as the number of particles and the initialization strategy affect the initial population diversity, the convergence speed toward the maximum power point, and the computational cost of the algorithm, defining the trade-off between speed and accuracy. Despite the extensive research in this field, there is still no clear consensus regarding the most suitable PSO parameter configuration for MPPT applications. This paper presents a statistical analysis of PSO parameter selection in MPPT applications, identifying the most frequently adopted parameter configurations and trends reported in the literature. The findings provide useful guidelines for researchers to select the PSO parameters according to different operating conditions, particularly under partial shading and irradiance variations. From a sustainability perspective, improving MPPT performance contributes to maximizing PV energy harvesting, reducing energy losses, and enhancing the reliability of PV systems, thereby supporting the transition toward more sustainable energy generation.

Parkinson’s disease is a progressive neurodegenerative condition whose prevalence has significantly increased. This work proposes the development of a severity index to classify patients from symptoms, mainly motor ones, using an Artificial Neuronal Network (ANN) trained by the Self-Organizing Maps (SOMs) algorithm. The FOX Insight database was used, which offers data in the form of questionnaires answered by patients or caregivers from all over the world, with information regarding this pathology. After pre-processing the data, a set of 597 questionnaires containing 28 defined questions was selected. The symptoms were individually analyzed after mapping and divided into four classes. In class 1, most symptoms were not present. In class 2, the presence of certain symptoms demonstrated early milestones of the disease. In class 3, symptoms related to the patient’s mobility, in particular pain, stand out among the most reported. In class 4, the intense presence of all symptoms is observed. To test the tool, data were used from some of these patients, who answered the same questionnaire at different times (simulating medical appointments). The presented severity index to classify patients allowed identifying the current stage of the disease allowing the follow-up. This AI-based decision-support tool can help medical professionals to predict the evolution of Parkinson’s disease, which can result in longer life quality of patients, in terms of symptoms and medication requirements.

Parkinson’s disease, a progressive neurodegenerative disorder of the motor system, shows non-motor symptoms up to 10 years before classic motor signs, highlighting the importance of early detection for effective treatment. This study proposes a severity index using an Artificial Neural Network (ANN) trained by the Self-Organizing Maps (SOM) algorithm, with data from the FOX Insight database. After pre-processing, 41,892 questionnaires were selected, covering 25 questions about non-motor symptoms, defined by a neurologist, and divided into four classes representing stages of the disease. The goal is to offer a tool to classify patients based on these symptoms, allowing for accurate monitoring and personalized interventions. Validation was carried out with data from patients responding to the questionnaire at spaced moments, simulating medical consultations. The study was successful in developing the severity index, highlighting the importance of gastrointestinal and urinary symptoms at different stages. The persistence of difficulty sleeping in group 3 indicates special attention must be paid to this symptom in the initial stages. These results highlight the clinical and practical relevance of the index, although more studies with real patients are needed for validation.