No abstract

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 as the next‐generation battery system have an ultrahigh theoretical energy density. However, the limited conversion of polysulfides in sulfur cathodes deteriorates the performance of Li‐S batteries. In this study, we develop a novel titanium nitride (TiN) catalyst for sulfur cathodes via atomic layer deposition (ALD). The synthesized ALD‐TiN catalyst shows controllable ultrafine particle size (<2 nm) and uniform distribution at the nanoscale in the carbon matrix. Combined with electrochemical analysis and multiple post‐characterization techniques, ALD‐TiN demonstrates an excellent catalytic effect to facilitate the nucleation and deposition of Li2S, which effectively suppresses the dissolution and shuttle of polysulfides. The as‐prepared sulfur cathodes, with the assistance of TiN catalyst, exhibit excellent cycling performance at a high rate (4 C) and deliver 200% higher discharge capacity than the pristine Sulfur‐pristine porous carbon composite cathodes.