Ahsan Zafar

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.

This paper investigated the state of corporate social responsibility (CSR) practices among Chinese companies operating in Pakistan. Findings reveal that while most Chinese firms have dedicated CSR programs, the strategic focus and long-term developmental impacts vary; enhancing corporate reputation rather than creating ethical value or empowering local communities was the dominant motivation. Key CSR activity areas include education, healthcare, and environmental initiatives, aligned with local socioeconomic needs. Although positive impacts are acknowledged, local stakeholders perceive CSR effects to be limited lacking strategic direction; challenges around political sensitivities, security risks, and coordination issues were highlighted. The paper advocates for a more strategic, participatory and sustainable CSR approach focused on long-term capacity building. Guidance frameworks attuned to local contexts can enable companies to balance business goals with lasting social value.

During the fourth energy revolution, the integration of Artificial Intelligence (AI) across various technological fields is critical to meet rising energy demands and address the depletion of fossil fuel reserves, leading to the adoption of smart grids. This study aims to enhance power generation capacity and minimize losses in smart grids by accurately predicting parameters. Traditional power grid stations transitioning to smart grids require precise parameter predictions. To achieve this, we employed AI-based machine learning models, specifically Random Forest (RF) and Long Short-Term Memory (LSTM), to predict the parameters of a solar power plant. After initial analysis through graphical visualization, we further refined the LSTM model using an advanced technique: Convolutional Neural Network (CNN-LSTM). Comparative results indicate that the CNN-LSTM model outperforms both the LSTM and RF models. For daily power generation, the CNN-LSTM achieved the lowest Mean Absolute Error (MAE) of 0.1335 and Mean Squared Error (MSE) of 0.0497. Consequently, the application of AI in this study significantly improves the accuracy of parameter prediction, enhancing the performance of basic machine learning models. This advancement supports the development of a robust and efficient power system that reduces power losses and boosts production capacity within the framework of smart grids.

During the fourth energy revolution, the integration of Artificial Intelligence (AI) across various technological fields is critical to meet rising energy demands and address the depletion of fossil fuel reserves, leading to the adoption of smart grids. This study aims to enhance power generation capacity and minimize losses in smart grids by accurately predicting parameters. Traditional power grid stations transitioning to smart grids require precise parameter predictions. To achieve this, we employed AI-based machine learning models, specifically Random Forest (RF) and Long Short-Term Memory (LSTM), to predict the parameters of a solar power plant. After initial analysis through graphical visualization, we further refined the LSTM model using an advanced technique: Convolutional Neural Network (CNN-LSTM). Comparative results indicate that the CNN-LSTM model outperforms both the LSTM and RF models. For daily power generation, the CNN-LSTM achieved the lowest Mean Absolute Error (MAE) of 0.1335 and Mean Squared Error (MSE) of 0.0497. Consequently, the application of AI in this study significantly improves the accuracy of parameter prediction, enhancing the performance of basic machine learning models. This advancement supports the development of a robust and efficient power system that reduces power losses and boosts production capacity within the framework of smart grids.

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.

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.