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Proposing a new precise price forecasting method is still a challenging task as electricity price signals generally exhibit various complex features. In this paper, a new approach for electricity price forecasting called hybrid local general regression neural network, and harmony search algorithm (LGRNN-HSA) is proposed. The proposed LGRNN-HSA is developed by combining the coordinate delay (CD) method, local forecasting paradigm, general regression neural network (GRNN) and harmony search algorithm (HSA). The CD is employed in the proposed method to reconstruct the time series dataset. Then the local forecasting paradigm is utilized with GRNN to predict the future price based on the nearest neighbours only so that the shortcomings in global forecasting methods can be overcome. To enhance the forecasting accuracy, the HSA is employed to optimize not only the parameters of local forecasting paradigm but also the smooth parameter which has a great effect on the accuracy of GRNN. In LGRNN-HSA, a different smooth parameter for every training point is utilized instead of utilizing a constant value in GRNN. To verify the forecasting accuracy of the LGRNN-HSA, a real-world price and demand dataset is employed. The simulation results prove that the LGRNN-HSA significantly improved forecasting accuracy compared to other methods.

Long-term load forecasting (LTLF) is an essential process for strategical planning of the future needed extension in the power systems of any country. Besides, deep learning has become the heart of the machine learning paradigm, which is wildly used nowadays in many fields, and it also has become the current revolution in Artificial Intelligence (AI). In this paper, an optimized deep learning model based on Stacked Long Short-Term Memory Network (SLSTMN) is proposed. The architecture of the model is optimized to get the best configuration using Genetic Algorithm (GA). In addition, the hyperparameters of the model network are optimized using many deep learning techniques. During the optimization process, hundreds of model configurations are tested. The accuracy of this model is compared with many deep learning models and is compared against the related work in the field of LTLF. The dataset of the South Australia State (SA) power system is used to test the compared models. This data includes maximum daily load, daily maximum temperature, daily minimum temperature, weekday, the month, and holidays for 12 years from 2005 to 2016. SLSTMN achieves excellent accuracy and the lowest error value (almost 1%) when compared with other models on the same dataset and with related work models on different datasets.

Detection of transformer faults avoids the transformer’s undesirable loss from service and ensures utility service continuity. Diagnosis of transformer faults is determined using dissolved gas analysis (DGA). Several traditional DGA techniques, such as IEC code 60599, Rogers’ ratio method, Dornenburg method, Key gas method, and Duval triangle method, but these DGA techniques suffer from poor diagnosis transformer faults. Therefore, more research was used to diagnose transformer fault and diagnostic accuracy by combining traditional DGA techniques with artificial intelligence and optimization techniques. In this paper, a proposed Adaptive Dynamic Polar Rose Guided Whale Optimization algorithm (AD-PRS-Guided WOA) improves the classification techniques’ parameters that were used to enhance the transformer diagnostic accuracy. The results showed that the proposed AD-PRS-Guided WOA provides high diagnostic accuracy of transformer faults as 97.1%, which is higher than other DGA techniques in the literature. The statistical analysis based on different tests, including ANOVA and Wilcoxon’s rank-sum, confirms the algorithm’s accuracy.