During the fourth energy revolution, artificial intelligence implementation is necessary in all fields of technology to meet the increasing energy demands and address the diminishing fossil fuel reserves, necessitating the shift towards smart grids. The authors focus on predicting parameters accurately to minimise loss and improve power generation capacity in smart grids, given that accurate parameter prediction is essential for traditional power grid stations converting to smart grids. The authors employ an artificial intelligence‐based machine learning model, namely the long short‐term memory, to predict parameters of a solar power plant. After analysing the results obtained from the long short‐term memory model in graphical visualisation, the model is further improved using two different techniques namely, a convolutional neural network‐long short‐term memory and the authors proposed an autoencoder long short‐term memory. Comparing the results of these models, the study finds that autoencoder long short‐term memory outperforms the convolutional neural network‐long short‐term memory as well as simple long short‐term memory. Thus, the use of artificial intelligence in this study substantially enhances the precision of parameter prediction by augmenting the performance of rudimentary machine learning models, thereby facilitating the attainment of a resilient and resourceful power system that overcomes power losses and ameliorates production capacity in the context of Smart Grids.

This study explores the implementation of advanced machine learning techniques to enhance the integration of renewable energy into smart grids, focusing specifically on predicting solar power generation for the upcoming year. Three distinct machine learning models are employed: the Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM) and a hybrid model that combines Autoencoder and Long Short-Term Memory (AE-LSTM). Using real-time solar power production data spanning a year, these models are trained and evaluated using mean absolute error (MAE) and mean squared error (MSE) as performance metrics. Results highlight the superior accuracy of the hybrid AE-LSTM model compared to the LSTM model as well as Bi-LSTM model, attributed to its capability to capture intricate temporal patterns and correlations within the data. This research underscores the significant potential of machine learning techniques, particularly the hybrid AE-LSTM approach, in facilitating the seamless integration of renewable energy resources into smart grids, contributing to more efficient and environmentally conscious power systems. Furthermore, a comprehensive analysis reveals the hybrid AE-LSTM model’s capacity to produce superior predictions due to its additional training, solidifying its advantage over models solely reliant on the other model’s architecture. In summary, this study demonstrates the effectiveness of advanced machine learning methodologies in revolutionizing renewable energy integration, with the hybrid AE-LSTM model standing out as a promising avenue for enhanced prediction accuracy.

Power utilities rely on accurate load forecasting for making effective real-time decisions and managing system operations, which, in turn, significantly impacts their financial performance. Accurate load forecasting enables these utilities to reduce operational costs, optimize generation, and maintain system stability. However, despite extensive research efforts, the nonlinear characteristics of power system and complex nature of the load datasets make accurate load forecasting challenging. This paper presents a novel application of the new Kolmogorov-Arnold Networks (KANs) for short-term load forecasting (STLF). KANs offer a promising predictive ability compared to Multi-Layer Perceptrons by replacing their linear weights with unique learnable activation functions. These learnable activation functions enable KAN to dynamically learn and effectively capture the non-linearities and complexities in power system load data. The proposed network is applied for STLF based on the latest ISO-NE hourly sampled load data in the period 2019–2023. The results demonstrate that KANs deliver higher accuracy in forecasting load over various prediction horizons while utilizing significantly reduced number of learnable parameters. This efficiency shows not only the KAN's predictive power but also a reduced computational complexity. By leveraging the dynamic learning capabilities of KAN, power utilities can achieve more precise load forecasts, leading to optimized operations and improved financial performance.

Incorporating zero-carbon emission sources of energy into the electric grid is essential to meet the growing energy needs in public and industrial sectors. Smart grids, with their cutting-edge sensing and communication technologies, provide an effective approach to integrating renewable energy resources and managing power systems efficiently. Improving solar energy efficiency remains a challenge within smart grid infrastructures. Nonetheless, recent progress in artificial intelligence (AI) techniques presents promising opportunities to improve energy production control and management. In this study, initially, we employed two different Machine learning (ML) models: Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM), to forecast solar power plant parameters. The analysis revealed that the LSTM model performed better than RNN in terms of Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Mean Squared Error (MSE). Following a review of the LSTM model’s graphical results, it was further enhanced by combining Autoencoder with LSTM, creating the Autoencoder LSTM (AELSTM) model. Next, a new hybrid model was introduced: Convolutional Neural Network-Autoencoder Long Short-Term Memory (HCAELSTM), designed to boost prediction accuracy. These models were trained on a one-year real-time solar power plant dataset for training and performance assessment. Ultimately, the hybrid HCAELSTM model surpassed the AELSTM model in terms of MAPE, MAE, and MSE. It excelled in MAPE scores for Daily Power Production, Peak Grid Power Production, and Solar Radiance, achieving low scores of 1.175, 2.116, and 1.592 respectively, demonstrating superior accuracy. The study underscores the importance of AI and ML, in particular, the hybrid model HCAELSTM, in enhancing the smart grid’s ability to integrate renewable energy sources. The hybrid model excels at accurately forecasting key measurements, improving solar power generation efficiency within the smart grid system which also plays a key role in the broader shift toward the fourth energy revolution.

Efficient hydrogen storage for automobiles necessitates materials with high storage capacities, moderate dehydrogenation temperatures, and rapid kinetics for desorption and absorption. However, there are presently no known materials that exhibit all of these qualities and can be reversed. In this presentation, we provide a summary of our recent endeavours focused on creating a fundamental computational method for identifying new hydrogen storage materials. To ensure effectiveness, this technique necessitates the following essential capabilities: (i) precise forecasting of thermodynamics related to decomposition, (ii) anticipation of crystal structures for hydrides that are not yet identified, and (iii) prediction of preferred decomposition and dehydrogenation temperatures. This study demonstrates the capability of atomistic DFT modelling in identifying new materials suitable for hydrogen storage applications.