The massive installation of renewable energy sources together with energy storage in the power grid can lead to fluctuating energy consumption when there is a bi-directional power flow due to the surplus of electricity generation. To ensure the security and reliability of the power grid, high-quality bi-directional power flow prediction is required. However, predicting bi-directional power flow remains a challenge due to the ever-changing characteristics of power flow and the influence of weather on renewable power generation. To overcome these challenges, we present two of the most popular hybrid deep learning (HDL) models based on a combination of a convolutional neural network (CNN) and long-term memory (LSTM) to predict the power flow in the investigated network cluster. In our approach, the models CNN-LSTM and LSTM-CNN were trained with two different datasets in terms of size and included parameters. The aim was to see whether the size of the dataset and the additional weather data can affect the performance of the proposed model to predict power flow. The result shows that both proposed models can achieve a small error under certain conditions. While the size and parameters of the dataset can affect the training time and accuracy of the HDL model.

This article presents the selection of an appropriate deep learning Long Short-Term Memory (LSTM) based probabilistic hour-ahead forecasting model for a grid connected industrial solar PV power plant located in Poland. It has a 317 kW peak power capacity and is connected with a metallurgical plant producing steel for car parts. The purpose of the study is to present a model that could be used by the plant to participate in the Polish intra-day electricity market. Four different LSTM models were investigated which include the vanilla model, the stacked LSTM model, the Bi-directional LSTM model and the LSTM-Autoencoder model. Out of the investigated models it was observed that the LSTM-Autoencoder model was the best performing one in terms of reliability. The average Root Mean Squared Error (RMSE) and the average Mean Absolute Error (MAE) for the Autoencoder model over 100 runs were 15.59 kW and 8.36 kW which represent 4.9% and 2.6% of the peak power respectively. Moreover, it was observed that it has the shortest width for the 95% confidence interval of only 0.5% for both the RMSE and the MAE. In terms of accuracy the best performing model was the LSTM bi-directional model with the average RMSE and MAE values of 12.87 kW and 6.91 kW which represent 4% and 2.1% of the peak power. The 95% confidence intervals width for both the RMSE and MAE over the 100 runs were 0.8% and 0.5% respectively.

In this article, a case study is presented on applying cluster analysis techniques to evaluate the level of power quality (PQ) parameters of a virtual power plant. The conducted research concerns the application of the K-means algorithm in comparison with the agglomerative algorithm for PQ data, which have different sizes of features. The object of the study deals with the standardized datasets containing classical PQ parameters from two sub-studies. Moreover, the optimal number of clusters for both algorithms is discussed using the elbow method and a dendrogram. The experimental results show that the dendrogram method requires a long processing time but gives a consistent result of the optimal number of clusters when there are additional parameters. In comparison, the elbow method is easy to compute but gives inconsistent results. According to the Calinski–Harabasz index and silhouette coefficient, the K-means algorithm performs better than the agglomerative algorithm in clustering the data points when there are no additional features of PQ data. Finally, based on the standard EN 50160, the result of the cluster analysis from both algorithms shows that all PQ parameters for each cluster in the two study objects are still below the limit level and work under normal operating conditions.

The transition from internal combustion engine vehicles to electric vehicles (EVs) is gaining momentum due to their significant environmental and economic benefits. This study addresses the challenges of integrating renewable energy sources, particularly solar power, into EV charging infrastructures by using deep learning models to predict photovoltaic (PV) power generation and EV charging demand. The study determines the optimal battery energy storage capacity and charging schedule based on the prediction result and actual data. A dataset of a 15 kWp rooftop PV system and simulated EV charging data are used. The results show that simple RNNs are most effective at predicting PV power due to their adept handling of simple patterns, while bidirectional LSTMs excel at predicting EV charging demand by capturing complex dynamics. The study also identifies an optimal battery storage capacity that will balance the use of the grid and surplus solar power through strategic charging scheduling, thereby improving the sustainability and efficiency of solar energy in EV charging infrastructures. This research highlights the potential for integrating renewable energy sources with advanced energy storage solutions to support the growing electric vehicle infrastructure.

Electric load forecasting is crucial for the metallurgy industry because it enables effective resource allocation, production scheduling, and optimized energy management. To achieve an accurate load forecasting, it is essential to develop an efficient approach. In this study, we considered the time factor of univariate time-series data to implement various deep learning models for predicting the load one hour ahead under different conditions (seasonal and daily variations). The goal was to identify the most suitable model for each specific condition. In this study, two hybrid deep learning models were proposed. The first model combines variational mode decomposition (VMD) with a convolutional neural network (CNN) and gated recurrent unit (GRU). The second model incorporates VMD with a CNN and long short-term memory (LSTM). The proposed models outperformed the baseline models. The VMD–CNN–LSTM performed well for seasonal conditions, with an average RMSE of 12.215 kW, MAE of 9.543 kW, and MAPE of 0.095%. Meanwhile, the VMD–CNN–GRU performed well for daily variations, with an average RMSE value of 11.595 kW, MAE of 9.092 kW, and MAPE of 0.079%. The findings support the practical application of the proposed models for electrical load forecasting in diverse scenarios, especially concerning seasonal and daily variations.

A regional grid cluster proposal is required to tackle power grid complexities and evaluate the impact of decentralized renewable energy generation. However, implementing regional grid clusters poses challenges in power flow forecasting owing to the inherent variability of renewable power generation and diverse power load behavior. Accurate forecasting is vital for monitoring the imported power during peak regional load periods and surplus power generation exported from the studied region. This study addressed the challenge of multistep bidirectional power flow forecasting by proposing an LSTM autoencoder model. During the training stage, the proposed model and baseline models were developed using autotune hyperparameters to fine-tune the models and maximize their performance. The model utilized the last 6 h leading up to the current time (24 steps of 15 min intervals) to predict the power flow 1 h ahead (4 steps of 15 min intervals) from the current time. In the model evaluation stage, the proposed model achieved the lowest RMSE and MAE scores with values of 32.243 MW and 24.154 MW, respectively. In addition, it achieved a good R² score of 0.93. The evaluation metrics demonstrated that the LSTM autoencoder outperformed the other models for multistep forecasting task in a regional grid cluster proposal.