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A new pre-processing methodology for gridded Satellite Precipitation Products (SPPs) is developed to improve the performance of Machine Learning (ML) algorithms for runoff prediction. The developed approach was applied to capture the rainfall patterns, and to select relevant input data. This approach was tested using the FeedForward Neural Network (FFNN) and the Extreme Learning Machine (ELM) given their flexibility and ability in hydrological modelling. The methodology was tested in a semiarid transboundary watershed located in North Africa (Algeria, Tunisia) with the Integrated MultisatellitE Retrievals for Global Precipitation Measurement (GPMIMERG) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) products. The results demonstrate the effectiveness of the proposed approach using all employed SPPs. In terms of Nash-Sutcliffe efficiency, the suggested pre-processing technique improved the prediction ability of FFNN by 13%, and of ELM by 15%, which highlights how pre-processing techniques significantly enhance ML models with SPP data. [ABSTRACT FROM AUTHOR]

Accurate photovoltaic (PV) energy forecasting plays a crucial role in the efficient operation of PV power stations. This study presents a novel hybrid machine-learning (ML) model that combines Gaussian process regression with wavelet packet decomposition to forecast PV power half an hour ahead. The proposed technique was applied to the PV energy database of a station located in Algeria and its performance was compared to that of traditional forecasting models. Performance evaluations demonstrate the superiority of the proposed approach over conventional ML methods, including Gaussian process regression, extreme learning machines, artificial neural networks and support vector machines, across all seasons. The proposed model exhibits lower normalized root mean square error (nRMSE) (2.116%) and root mean square error (RMSE) (208.233 kW) values, along with a higher coefficient of determination (R 2) of 99.881%. Furthermore, the exceptional performance of the model is maintained even when tested with various prediction horizons. However, as the forecast horizon extends from 1.5 to 5.5 hours, the prediction accuracy decreases, evident by the increase in the RMSE (710.839 kW) and nRMSE (7.276%), and a decrease in R 2 (98.462%). Comparative analysis with recent studies reveals that our approach consistently delivers competitive or superior results. This study provides empirical evidence supporting the effectiveness of the proposed hybrid ML model, suggesting its potential as a reliable tool for enhancing PV power forecasting accuracy, thereby contributing to more efficient grid management. [ABSTRACT FROM AUTHOR]

Electricity generation from photovoltaic (PV) in Latvia is currently below the necessary capacity required to contribute to achieving climate neutrality by 2050. However, photovoltaic offer households and companies the opportunity to align their electricity consumption with environmental goals while supporting the European Green Deal objectives. In recent years, there was a growing interest in the installation of photovoltaic microgenerators in Latvia. By June 2023, the total number of households microgenerators connected to the grid has reached 15,000, boasting a collective production capacity exceeding 120 MW. This significant increase is in stark contrast to the situation 5 years ago when Latvia had a mere 3 MW of photovoltaic capacity. Several factors contributed to this growth, including a surge in electricity prices, at times surging more than tenfold, and the implementation of renewable energy (RE) use support measures to facilitate the acquisition of RE systems. Furthermore, 2023 witnessed numerous amendments to Latvia's energy policy documents and the introduction of the concept of citizen energy communities (CEC), indicating a growing emphasis on harnessing solar energy potential in the country. However, it remains crucial to base these endeavors on accurate, economically viable information regarding solar technologies, their costs and their anticipated long-term outcomes. This manuscript aims to provide an overview of the grid-connected potential of rooftop photovoltaic systems within a Latvian urban setting. Through extensive research, a model has been developed, employing a thoroughly tested simulation program for evaluating the generation capacity of photovoltaic systems. This model considers real electricity consumption data, the existing infrastructure and economic factors. The findings affirm the technical and economic viability of urban rooftop photovoltaic systems within the Latvian context. It has been established that the implementation of such citizen energy communities energy systems holds significant potential. These systems have the potential to be a promising solution for future electricity generation, addressing some of the demands while relieving strain on external power grids. However, the full potential can only be realized with improved infrastructure, and the system's profitability is heavily contingent on market dynamics and political conditions. This study may also be applicable to other photovoltaic systems facing similar climate conditions. [ABSTRACT FROM AUTHOR]

Accurate measurements of solar radiation are required to ensure that power and energy systems continue to function effectively and securely. On the other hand, estimating it is extremely challenging due to the non-stationary behaviour and randomness of its components. In this research, a novel hybrid forecasting model, namely complete ensemble empirical mode decomposition with adaptive noise–Gaussian process regression (CEEMDAN–GPR), has been developed for daily global solar radiation prediction. The non-stationary global solar radiation series is transformed by CEEMDAN into regular subsets. After that, the GPR model uses these subsets as inputs to perform its prediction. According to the results of this research, the performance of the developed hybrid model is superior to two widely used hybrid models for solar radiation forecasting, namely wavelet–GPR and wavelet packet–GPR, in terms of mean square error, root mean square error, coefficient of determination and relative root mean square error values, which reached 3.23 MJ/m2/day, 1.80 MJ/m2/day, 95.56%, and 8.80%, respectively (for one-step forward forecasting). The proposed hybrid model can be used to ensure the safe and reliable operation of the electricity system. [ABSTRACT FROM AUTHOR]