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Kaneko N., Okazawa K., Zhao D., et al. Non-intrusive thermal load disaggregation and forecasting for effective HVAC systems. Applied Energy 367, 123379 (2024); https://doi.org/10.1016/j.apenergy.2024.123379., Non-Intrusive Thermal Load Monitoring (NITLM) tracks the sub-loads generated by each heat source (e.g. occupants, equipment, solar radiation etc.) from the total thermal load and indirectly provides a room's thermal properties without additional sensors. Since sub-loads can improve the efficiency of HVAC systems, NITLM is a very attractive technology for building energy management. NITLM has traditionally focused on analyzing past and present sub-loads. However, by forecasting future sub-loads, HVAC systems will be able to schedule operations that take into account the thermal properties of future rooms. This work focuses on a new NITLM framework that forecasts future sub-loads based on the current and past total thermal loads. In experiments, we selected occupant loads that are closely connected to HVAC systems and performed sub-load forecasting using two types of approaches. One is a two-step approach that separately performs them in turn. This approach use separately trained model for disaggregation and forecasting, this allow us to fine-tuning the hyper-parameter for dedicate model. Moreover, the two-step approach can take into account the different properties and difficulties of each inference, resulting in smaller errors in sub-load forecasting. The other is an integrated approach. This approach combines load disaggregation and forecasting into a single estimation process, eliminating error propagation and reducing overall error in sub-load forecasting. Moreover, this approach utilizes the Adaptive Moment Estimation (Adam) algorithm for effective parameter optimization, enabling complex training and improving the accuracy of sub-load forecasting. We conducted evaluations of thermal load disaggregation and forecasting across a range of realistic building scenarios. The findings indicate that the integrated approach predicts sub-loads with a MAE that is up to 34.9% lower than that of the two-step approach. Additionally, it identifies occupants presence with an 1

Kaneko N., Okazawa K., Zhao D., et al. Non-intrusive thermal load disaggregation and forecasting for effective HVAC systems. Applied Energy 367, 123379 (2024); https://doi.org/10.1016/j.apenergy.2024.123379., Non-Intrusive Thermal Load Monitoring (NITLM) tracks the sub-loads generated by each heat source (e.g. occupants, equipment, solar radiation etc.) from the total thermal load and indirectly provides a room's thermal properties without additional sensors. Since sub-loads can improve the efficiency of HVAC systems, NITLM is a very attractive technology for building energy management. NITLM has traditionally focused on analyzing past and present sub-loads. However, by forecasting future sub-loads, HVAC systems will be able to schedule operations that take into account the thermal properties of future rooms. This work focuses on a new NITLM framework that forecasts future sub-loads based on the current and past total thermal loads. In experiments, we selected occupant loads that are closely connected to HVAC systems and performed sub-load forecasting using two types of approaches. One is a two-step approach that separately performs them in turn. This approach use separately trained model for disaggregation and forecasting, this allow us to fine-tuning the hyper-parameter for dedicate model. Moreover, the two-step approach can take into account the different properties and difficulties of each inference, resulting in smaller errors in sub-load forecasting. The other is an integrated approach. This approach combines load disaggregation and forecasting into a single estimation process, eliminating error propagation and reducing overall error in sub-load forecasting. Moreover, this approach utilizes the Adaptive Moment Estimation (Adam) algorithm for effective parameter optimization, enabling complex training and improving the accuracy of sub-load forecasting. We conducted evaluations of thermal load disaggregation and forecasting across a range of realistic building scenarios. The findings indicate that the integrated approach predicts sub-loads with a MAE that is up to 34.9% lower than that of the two-step approach. Additionally, it identifies occupants presence with an 1

Watari D., Taniguchi I., Onoye T.. Duck Curve Aware Dynamic Pricing and Battery Scheduling Strategy Using Reinforcement Learning. IEEE Transactions on Smart Grid , (2023); https://doi.org/10.1109/TSG.2023.3288355., The duck curve is becoming a global problem in energy technology due to the rapid increase in solar power adoption and the rise of prosumers. To address this issue, a resource aggregator (RA) has emerged to provide flexible solutions through aggregating the prosumers and demand response such as dynamic pricing. This paper proposes an optimal strategy for the RA that dispatches dynamic pricing to the prosumers and leverages the battery system at both RA and prosumer levels. The proposed method is based on a model-free deep reinforcement learning (DRL) algorithm to optimize each prosumer’s retail prices and schedule usage of the RA’s battery power station. An objective reward function is used to maximize the RA’s profit, minimize the prosumer’s cost, and maximize the improvement of the duck curve. The performance of the proposed DRL-based strategy was demonstrated by simulation experiments using actual wholesale price, demand, and PV generation data. The results show that the proposed strategy can improve the standard deviation and peak-to-average ratio of net load by up to 57.1% and 23%, respectively.

Watari D., Taniguchi I., Goverde H., et al. Multi-time scale energy management framework for smart PV systems mixing fast and slow dynamics. Applied Energy 289, 116671 (2021); https://doi.org/10.1016/j.apenergy.2021.116671., We propose a multi-time scale energy management framework for a smart photovoltaic (PV) system that can calculate optimized schedules for battery operation, power purchases, and appliance usage. A smart PV system is a local energy community that includes several buildings and households equipped with PV panels and batteries. However, due to the unpredictability and fast variation of PV generation, maintaining energy balance and reducing electricity costs in the system is challenging. Our proposed framework employs a model predictive control approach with a physics-based PV forecasting model and an accurately parameterized battery model. We also introduce a multi-time scale structure composed of two-time scales: a longer coarse-grained time scale for daily horizon with 15-minutes resolution and a shorter fine-grained time scale for 15-minutes horizon with 1-second resolution. In contrast to the current single-time scale approaches, this alternative structure enables the management of a necessary mix of fast and slow system dynamics with reasonable computational times while maintaining high accuracy. Simulation results show that the proposed framework reduces electricity costs up 48.1% compared with baseline methods. The necessity of a multi-time scale and the impact on accurate system modeling in terms of PV forecasting and batteries are also demonstrated.

Watari D., Taniguchi I., Onoye T.. Duck Curve Aware Dynamic Pricing and Battery Scheduling Strategy Using Reinforcement Learning. IEEE Transactions on Smart Grid , (2023); https://doi.org/10.1109/TSG.2023.3288355., The duck curve is becoming a global problem in energy technology due to the rapid increase in solar power adoption and the rise of prosumers. To address this issue, a resource aggregator (RA) has emerged to provide flexible solutions through aggregating the prosumers and demand response such as dynamic pricing. This paper proposes an optimal strategy for the RA that dispatches dynamic pricing to the prosumers and leverages the battery system at both RA and prosumer levels. The proposed method is based on a model-free deep reinforcement learning (DRL) algorithm to optimize each prosumer’s retail prices and schedule usage of the RA’s battery power station. An objective reward function is used to maximize the RA’s profit, minimize the prosumer’s cost, and maximize the improvement of the duck curve. The performance of the proposed DRL-based strategy was demonstrated by simulation experiments using actual wholesale price, demand, and PV generation data. The results show that the proposed strategy can improve the standard deviation and peak-to-average ratio of net load by up to 57.1% and 23%, respectively.