• Energy Research

The use of image analysis has often been suggested as a practical way to monitor the soiling accumulated on the surfaces of solar energy conversion devices. Indeed, the deposited soiling particles can be counted and characterized to calculate the area they cover, and this area can be converted into an energy loss. However, several particle counting methodologies exist and can lead to dissimilar results. This work focuses on the role of thresholding, an essential step where particles are distinguished from a background based on the pixel brightness. Sixteen automatic thresholding methods are assessed using 13 200 micrographs of glass coupons soiled at nine locations globally. In low‐to‐intermediate soiling conditions, the “Triangle” method is found to return the minimum coefficient of variation and a mean deviation closer to zero. On the other hand, methods assuming a bimodal distribution of pixel brightness underestimate the area coverage. In addition, since soiling can be unevenly distributed over a surface, different loss estimations can be returned when the same image analysis process is used on different spots on a sample's surface. For these reasons, image analysis should be repeated at multiple locations on each investigated surface.

An electric power network can be evolved into smart grids, which are measured by providing energy efficiency and improving the available resources. With the development of software and hardware elements, the decision-making mechanism of existing smart grids is transformed into more robust uninterrupted and economical energy management systems. In this study, a cognitive-based algorithm using dynamic energy management flexibility, storage and energy management algorithm and cloud computing architecture is proposed. Using this approach, an uninterrupted and economical energy management system can be planned. In addition, the proposed approach provides the optimization of supply and demand sides.

The increasing number of people and the huge industrial evolution have led to serious environmental consequences, which have made energy from Renewable Energy Sources (RESs) inevitable. However, these energy sources suffer from intermittency as they depend on natural sources, which change over time. Consequently, this research addresses the issue by proposing an ANN model for reliable renewable energy forecasting. The Encoding Long Short-Time Memory Decoding (ELSTMD) model consists of two encodings, one LSTM, and two decoding layers. Moreover, the introduced model and 21 time series forecasting models are evaluated, including statistical, Artificial Intelligence (AI), and hybrid approaches. While making predictions, the impact of 17 distinct features, categorized into five primary groups—electricity, weather, economic, and seasonality-related attributes, as well as natural disasters— has been analyzed. Thus, the models are trained as univariate time series with exogenous variables. To evaluate the model’s generalization, an additional dataset involving photovoltaic (PV) panels was used. The proposed model outperforms all competitors on both datasets, achieving an (R2, MAE, MSE, RMSE) of (0.56714, 0.06398, 0.00691, 0.08313) on the first dataset and (0.76574, 0.09344, 0.01417, 0.11903) on the second dataset. The most notable advantage of this research can be summarized as providing precise predictions for the electricity generated from RESs, which can help reduce the intermittency, randomness, and stability issues associated with these sources. It also plays a crucial role in optimizing electrical power systems’ planning and storage efficiency.