An optimized system for predicting energy usage in smart grids using temporal fusion transformer and Aquila optimizer

This research presents an optimized system for predicting energy usage in smart grids by integrating the Temporal Fusion Transformer (TFT) with the Aquila Optimizer (AO). The study addresses the growing need for accurate energy consumption forecasts in smart grids, driven by the increasing adoption of renewable energy and real-time data collection through smart meters. The TFT model leverages self-attention mechanisms to handle complex time-series data, improving forecasting accuracy across various time horizons. To enhance predictive performance, the Aquila Optimizer, a nature-inspired algorithm, is employed to fine-tune critical hyperparameters, ensuring optimal model convergence and performance. The proposed AO-TFT model is evaluated against traditional models like LSTM and CNN-BiLSTM, demonstrating superior accuracy, lower RMSE, and faster computation times. The research also analyses the impact of various factors, including building types, weather conditions, and load variations on energy prediction. The proposed AO-TFT model achieved a significantly lower RMSE of 0.48 and MAE of 0.31, demonstrating superior accuracy compared to traditional models. Future work is suggested to explore hybrid optimization techniques and real-time adaptive models for dynamic grid management

Degradation insight of organophosphate pesticide chlorpyrifos through novel intermediate 2,6-dihydroxypyridine by Arthrobacter sp. HM01

AbstractOrganophosphates (OPs) are hazardous pesticides, but an indispensable part of modern agriculture; collaterally contaminating agricultural soil and surrounding water. They have raised serious food safety and environmental toxicity that adversely affect the terrestrial and aquatic ecosystems and therefore, it become essential to develop a rapid bioremediation technique for restoring the pristine environment. A newly OPs degrading Arthrobacter sp. HM01 was isolated from pesticide-contaminated soil and identified by a ribotyping (16S rRNA) method. Genus Arthrobacter has not been previously reported in chlorpyrifos (CP) degradation, which shows 99% CP (100 mg L−1) degradation within 10 h in mMSM medium and also shows tolerance to a high concentration (1000 mg L−1) of CP. HM01 utilized a broad range of OPs pesticides and other aromatic pollutants including intermediates of CP degradation as sole carbon sources. The maximum CP degradation was obtained at pH 7 and 32 °C. During the degradation, a newly identified intermediate 2,6-dihydroxypyridine was detected through TLC/HPLC/LCMS analysis and a putative pathway was proposed for its degradation. The study also revealed that the organophosphate hydrolase (opdH) gene was responsible for CP degradation, and the opdH-enzyme was located intracellularly. The opdH enzyme was characterized from cell free extract for its optimum pH and temperature requirement, which was 7.0 and 50 °C, respectively. Thus, the results revealed the true potential of HM01 for OPs-bioremediation. Moreover, the strain HM01 showed the fastest rate of CP degradation, among the reported Arthrobacter sp. Graphical Abstract</jats:p

Additional file 1 of Degradation insight of organophosphate pesticide chlorpyrifos through novel intermediate 2,6-dihydroxypyridine by Arthrobacter sp. HM01

Additional file 1: Figure S1. Molecular docking of chlorpyrifos pesticide in catalytic pocket of opdH enzyme, where conserved residues were interacted with pesticides. Figure S2. HPLC chromatogram of standard intermediate 3,5,6-Trichloro-2-pyridinol (TCP) of chlorpyrifos pesticide