Due to severe climate change impact on electricity consumption, as well as new trends in smart grids (such as the use of renewable resources and the advent of prosumers and energy commons), medium-term and long-term electricity load forecasting has become a crucial need. Such forecasts are necessary to support the plans and decisions related to the capacity evaluation of centralized and decentralized power generation systems, demand response strategies, and controlling the operation. To address this problem, the main objective of this study is to develop and compare precise district level models for predicting the electrical load demand based on machine learning techniques including support vector machine (SVM) and Random Forest (RF), and deep learning methods such as non-linear auto-regressive exogenous (NARX) neural network and recurrent neural networks (Long Short-Term Memory—LSTM). A dataset including nine years of historical load demand for Bruce County, Ontario, Canada, fused with the climatic information (temperature and wind speed) are used to train the models after completing the preprocessing and cleaning stages. The results show that by employing deep learning, the model could predict the load demand more accurately than SVM and RF, with an R-Squared of about 0.93–0.96 and Mean Absolute Percentage Error (MAPE) of about 4–10%. The model can be used not only by the municipalities as well as utility companies and power distributors in the management and expansion of electricity grids; but also by the households to make decisions on the adoption of home- and district-scale renewable energy technologies.

Lithium–sulfur (Li-S) batteries hold great promise for next-generation energy storage, offering high theoretical energy density and cost-effectiveness. However, challenges like sulfur’s low conductivity, polysulfide dissolution, and significant volume changes limit their practical application. This study addresses these issues by investigating porosity-engineered carbon hosts, specifically potassium hydroxide (KOH)-activated Black Pearl carbons (BP2000, BP1300, and BP800). Varying KOH-to-carbon ratios allowed precise tailoring of micro- and mesoporous structures, optimizing sulfur loading, electrolyte infiltration, and ion transport. Composites were characterized by TGA, NLDFT, SEM, XRD, and FTIR and electrochemically (cycling, CV, EIS). The KOH-modified BP2000 1:1 cathode, exhibiting the highest mesopore volume increase, demonstrated superior electrochemical performance, including enhanced cycling stability, rate capability, and reduced charge-transfer resistance. These findings emphasize the importance of optimizing pore distribution in carbon hosts for high-performance Li-S batteries and provide valuable insights for advanced energy storage material design.