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This paper presents the analytical analysis of the sinusoidal ground electrode commonly used in Portugal by the Portuguese Electric Company. This electrode is easy to install, particularly for two layer soils with rocky bottom layer, and costs much less than it does a strip conductor. Both the theoretical results as well as measurements in the field have shown that the empirical model used by the company leads to large errors. Here the authors propose a new procedure to calculate the grounding resistance for this type of electrode using the average resistance between the wire and strip electrodes, which the calculation is well-established. To avoid heavy computation, the authors also propose the use of simple formulas in order to easily compute the strip resistance. The theoretical results and field measurements are compared and show the validity of the procedure being proposed here. [ABSTRACT FROM AUTHOR]

Short-Term Load Forecasting is critical for reliable power system operation, and the search for enhanced methodologies has been a constant field of investigation, particularly in an increasingly competitive environment where the market operator and its participants need to better inform their decisions. Hence, it is important to continue advancing in terms of forecasting accuracy and consistency. This paper presents a new deep learning-based ensemble methodology for 24 h ahead load forecasting, where an automatic framework is proposed to select the best Box-Jenkins models (ARIMA Forecasters), from a wide-range of combinations. The method is distinct in its parameters but more importantly in considering different batches of historical (training) data, thus benefiting from prediction models focused on recent and longer load trends. Afterwards, these accurate predictions, mainly the linear components of the load time-series, are fed to the ensemble Deep Forward Neural Network. This flexible type of network architecture not only functions as a combiner but also receives additional historical and auxiliary data to further its generalization capabilities. Numerical testing using New England market data validated the proposed ensemble approach with diverse base forecasters, achieving promising results in comparison with other state-of-the-art methods. [ABSTRACT FROM AUTHOR]