Increasing electricity demands with limited existing utility grid infrastructure has led to electricity consumers dealing with operational constraints such as contractual energy violation penalty and and time of day pricing of electricity. Therefore, integration of renewable energy such as solar power generation is popular choice to fulfil additional demands. The Integration of storage elements such as battery gives scope to optimize for contractual energy violation and energy purchase cost by ’day-ahead’ planning of battery charging schedules. However, the uncertainty of day ahead variables such as load/demand and solar generation pose problems in reasonable optimization. In this paper, the EV charging loads(for C&I consumers) are considered as uncertain day ahead variable. At first, a day-ahead forecasting of EV charging loads by training iTransformer based model was performed which offers Mean Absolute Error (MAE) of 2.93%. Further these load forecasts were fed to three optimization algorithms- Linear Programming (LP), Stochastic Optimization (SO), and Robust Optimization (RO) to establish trade off between contractual energy violations and grid energy purchase cost. The SO shows good trade-off performance in terms of 3-5% reduction in grid energy costs compared to LP and 20-70% reduction in contractual energy violation (i.e. peak shaving) compared to LP. Also, the RO shows best performance of 90% reduction in contractual energy violations and poorest performance for grid energy purchase cost minimization.

Missing data imputation of transportation occupancy data faces a big challenge, specifically in situations where the entire trip of a vehicle is missing due to no sensors. Traditional imputation algorithms such as k-nearest neighbor (KNN), Multi-recurrent Neural Network (MRNN), Bidirectional recurrent imputation for time series (BRITS), and Transformer-based algorithms like self-attention-based imputation for time series (SAITS) and time-aware-self-attention-based imputation for time series (TA-SAITS) usually have poor adaptiveness to dataset variations since their performances depend highly on dataset characteristics. Even typical ensemble techniques such as bagging, boosting, and stacking do not update dynamically regarding various patterns in the same dataset. To overcome those challenges, this work proposes an Adaptive Ensemble Imputation based on Deep Reinforcement Learning, AEI-DRL. AEI-DRL uses a sliding window approach to scroll over sub-parts of the data and learns the optimal weights assignment of multiple imputation methods. This enables AEI-DRL to dynamically adjust weightage towards alternative algorithms or their combinations for different data segments. Experimental results show improvements in imputation results using AEI-DRL. It achieved a 5.3% and 2.6% reduction in mean square error (MSE) on the transportation occupancy dataset compared with the second-best method. On a non-transport dataset, AEI-DRL was able to record a reduction of MSE 16%, hence proving its applicability across time-series datasets with all sorts of diverse missing data patterns.

Accurate occupancy estimation in transportation is essential for efficient fleet management, optimizing service quality, and ensuring smooth operations. The challenge arises when some trips are equipped with sensors while others operate without them, leading to incomplete datasets. Traditional and deep learning-based imputation methods rely on sensor-equipped trips to estimate occupancy for sensorless trips, but their accuracy suffers when sensor-equipped and sensorless trips are assigned randomly. To address this, a Deep Reinforcement Learning (DRL) approach is introduced to optimize the allocation of sensor-equipped and sensor-less trips within transportation timetables. By dynamically reorganizing trip assignments, this method significantly enhances the accuracy and reliability of imputation models. Results show that the DRL-based allocation approach reduces Mean Squared Error by 10% after optimization. This framework provides a practical and robust solution for managing incomplete datasets, effectively bridging the gap between theoretical imputation models and real-world transportation systems.