Search

Promoting flexible energy demand through response programs in residential neighborhoods would play a vital role in addressing the issues associated with increasing the share of distributed solar systems and balancing supply and demand in energy networks. However, accurately identifying baseline-related energy measurements when activating energy demand response events remains challenging. In response, this study presents a deep learning-based, data-driven framework to improve short-term estimates of demand response baselines during the activation of response events. This framework includes bidirectional long-term memory (BiLSTM), long-term memory (LSTM), gated recurrent unit (GRU), convolutional neural networks (CNN), deep neural networks (DNN), and recurrent neural networks (RNN). Their performance is evaluated by considering different aggregation levels of the demand response baseline profile for 337 dwellings in the city of La Rochelle, France, over different time horizons, not exceeding 24 h. It is also compared with fifteen traditional statistical and machine learning methods in terms of forecasting accuracy. The results demonstrated that deep learning-based models, compared to others, significantly succeeded in minimizing the gap between the actual and forecasted values of demand response baselines at all different aggregation levels of dwelling units over the considered time-horizons. BiLSTM models, followed by GRU and LSTM, consistently demonstrated the lowest mean absolute percentage error (MAPE) in most comparison experiments, with values up to 9.08%, 8.71%, and 9.42%, respectively. Compared to traditional statistical and machine learning models, extreme gradient boosting (XGBoost) was among the best, with a value up to 11.56% of MAPE, but could not achieve the same level of forecasting accuracy in all comparison experiments. Such high performance reveals the potential of the proposed deep learning approach and highlights its importance for improving short-term estimates of future baselines when implementing demand response programs in residential neighborhood contexts.

Promoting flexible energy demand through response programs in residential neighborhoods would play a vital role in addressing the issues associated with increasing the share of distributed solar systems and balancing supply and demand in energy networks. However, accurately identifying baseline-related energy measurements when activating energy demand response events remains challenging. In response, this study presents a deep learning-based, data-driven framework to improve short-term estimates of demand response baselines during the activation of response events. This framework includes bidirectional long-term memory (BiLSTM), long-term memory (LSTM), gated recurrent unit (GRU), convolutional neural networks (CNN), deep neural networks (DNN), and recurrent neural networks (RNN). Their performance is evaluated by considering different aggregation levels of the demand response baseline profile for 337 dwellings in the city of La Rochelle, France, over different time horizons, not exceeding 24 h. It is also compared with fifteen traditional statistical and machine learning methods in terms of forecasting accuracy. The results demonstrated that deep learning-based models, compared to others, significantly succeeded in minimizing the gap between the actual and forecasted values of demand response baselines at all different aggregation levels of dwelling units over the considered time-horizons. BiLSTM models, followed by GRU and LSTM, consistently demonstrated the lowest mean absolute percentage error (MAPE) in most comparison experiments, with values up to 9.08%, 8.71%, and 9.42%, respectively. Compared to traditional statistical and machine learning models, extreme gradient boosting (XGBoost) was among the best, with a value up to 11.56% of MAPE, but could not achieve the same level of forecasting accuracy in all comparison experiments. Such high performance reveals the potential of the proposed deep learning approach and highlights its importance for improving short-term estimates of future baselines when implementing demand response programs in residential neighborhood contexts. [ABSTRACT FROM AUTHOR]

Effecient green tools of β-(3,4-dichlorophenyl-acrylic acid, thioglycolic acid and β-(2-oxopyrrolidin-1-yl) ethanol under ultrasonic and microwave-assisted irradiation (MW-US) as found to be a simple approach for preparing interesting Spiro derivatives. A novel class Spiro-pyrrolidine-2,3'-thieno[2,3-d]pyridazine derivatives (2–6) were efficiently synthesized via one-pot multicomponent reactions. Ethoxy acetyl Spiro pyrrolidine-2,3'-thieno[2,3-d]pyridazin-hydrazide (5) assessed as ecofriendly microbicidal and insecticidal activities to establish a structure-activity relationship. Elemental analysis and spectroscopic data were used to describe the structures of all synthesized products. The Spiro-heterocyclic compounds are exclusive non-planar structures and branded for binding to biomolecules. The microbicidal and insecticidal activities of the compounds were investigated on ATCC coded test microorganisms upon the applied dose and time was studied through two kinetic study. The interaction with these antibacterial and insect's third instar larvae was medicinal and economically valuable. The preliminary testing demonstrated flourishing microbicidal capability to prevent the growth of threatening pathogenic bacteria as well as blossoming insecticidal efficacy. Moreover, Molecular docking and density functional theory simulations can be used to validate the experimental results. Also, we utilized the docking approach with secondary bacterial infection indicated that Ethoxy acetyl Spiro hydrazide (5) and Spiro arylidene hybrid (6) have exerted the strongest docking binding value against the active sites of 6LU7. [ABSTRACT FROM AUTHOR]