Search Results (4)

This research presents an advanced methodology for smart management of energy losses in electrical distribution networks by leveraging deep neural network architectures. The primary objective is to enhance the accuracy of short-term forecasting for nodal loads and corresponding energy losses, enabling more efficient and intelligent grid operation. Two predictive approaches were explored: the first involves separate forecasting of nodal loads followed by loss calculations, while the second directly estimates network-wide energy losses. For model implementation, Long Short-Term Memory (LSTM) networks and the enhanced Residual Network (eResNet) architecture, developed at the Institute of Electrodynamics of the National Academy of Sciences of Ukraine, were utilized. The models were validated using retrospective data from a Ukrainian Distribution System Operator (DSO) covering the period from 2017 to 2019 with 30 min sampling intervals. An adapted CIGRE benchmark medium-voltage network was employed to simulate real-world conditions. Given the presence of anomalies and missing values in the operational data, a two-stage preprocessing algorithm incorporating DBSCAN clustering was applied for data cleansing and imputation. The results indicate a Mean Absolute Percentage Error (MAPE) of just 3.29% for nodal load forecasts, which significantly outperforms conventional methods. These findings affirm the feasibility of integrating such models into Smart Grid infrastructures to improve decision-making, minimize operational losses, and reduce the costs associated with energy loss compensation. This study provides a practical framework for data-driven energy loss management, emphasizing the growing role of artificial intelligence in modern power systems. Full article

This study aims to analyse Tunisian farmers’ ability to pay (ATP) in a citrus area and propose a penalising price strategy based on the block-pricing process to decrease over-irrigation without affecting farmers’ incomes. The methodology is based on the residual imputation approach to determine farmers’ ATP, a stochastic production frontier to estimate the technical efficiency to determine optimal water irrigation quantity and calculation of the price elasticity of demand for an effective penalty and the Gini index before and after penalisation to study equity improvement. A survey was carried out on a sample of 147 citrus farms in the Nabeul Governorate, Northeastern Tunisia. The technical efficiency analysis confirms that an optimal quantity of 5000 m³/ha guarantees the maximisation of yields and profits. Above this quantity, the amount of overused water could be penalised without significantly affecting farmers’ incomes. Results also reveal that water overconsumption represents 28% of available resources and the ATP varies according to technical efficiency. Therefore, the proposed penalty system could reduce water overconsumption by 44.56% without deteriorating agricultural welfare. To improve water management as well as farmers’ welfare, this study recommends an increase in the technical efficiency level of farms to optimise all production factors for any implemented pricing policy. Full article

The operational management of tanks for urban water distribution networks is usually a critical element due to the dynamic nature of the water demand and the age of the distribution networks themselves. Today, in a context of water resource scarcity, optimal management is a key point for the sustainable management of urban systems. For this purpose, it is useful to implement predictive tools, able to provide short-term forecasts to inform urban water managers on the most suitable procedure to be applied in the case of routine or critical events. A possible approach is to use autoregressive integrated moving average (ARIMA) models, which combine the autoregression and the moving average approaches, with the possibility to work on a differenced series of the data. They can further embed a seasonal- component (Seasonal ARIMA models), to account for possible periodic patterns in the observed data. In this study, the data of water levels measured from May 2018 to 10 January 2019 in a water storage tank in the area of Benevento, Campania region (Italy), were considered as a case study. The standard ARIMA techniques were applied to find the best model for this dataset, according to “Deviance Information Criterion” (DIC) and “Bayesian Information Criterion” (BIC) optimization. The results are discussed, shedding light on the behaviour of the time series with reference to the management of the infrastructure and the dataset. The residual analysis, carried out to check if the autocorrelation was still present and if the residuals were normally distributed, revealed a narrow distribution. Small values were found throughout the dataset, except for a few periods, corresponding to the imputed data. This application represents a preliminary step of more detailed research that will be carried out to detect the best model for forecasting tank levels for the case study to help to manage the urban water supply. Full article

Accurate estimation of fine particulate matter with diameter ≤2.5 μm (PM_2.5) at a high spatiotemporal resolution is crucial for the evaluation of its health effects. Previous studies face multiple challenges including limited ground measurements and availability of spatiotemporal covariates. Although the multiangle implementation of atmospheric correction (MAIAC) retrieves satellite aerosol optical depth (AOD) at a high spatiotemporal resolution, massive non-random missingness considerably limits its application in PM_2.5 estimation. Here, a deep learning approach, i.e., bootstrap aggregating (bagging) of autoencoder-based residual deep networks, was developed to make robust imputation of MAIAC AOD and further estimate PM_2.5 at a high spatial (1 km) and temporal (daily) resolution. The base model consisted of autoencoder-based residual networks where residual connections were introduced to improve learning performance. Bagging of residual networks was used to generate ensemble predictions for better accuracy and uncertainty estimates. As a case study, the proposed approach was applied to impute daily satellite AOD and subsequently estimate daily PM_2.5 in the Jing-Jin-Ji metropolitan region of China in 2015. The presented approach achieved competitive performance in AOD imputation (mean test R²: 0.96; mean test RMSE: 0.06) and PM_2.5 estimation (test R²: 0.90; test RMSE: 22.3 μg/m³). In the additional independent tests using ground AERONET AOD and PM_2.5 measurements at the monitoring station of the U.S. Embassy in Beijing, this approach achieved high R² (0.82–0.97). Compared with the state-of-the-art machine learning method, XGBoost, the proposed approach generated more reasonable spatial variation for predicted PM_2.5 surfaces. Publically available covariates used included meteorology, MERRA2 PBLH and AOD, coordinates, and elevation. Other covariates such as cloud fractions or land-use were not used due to unavailability. The results of validation and independent testing demonstrate the usefulness of the proposed approach in exposure assessment of PM_2.5 using satellite AOD having massive missing values. Full article