The possibility of a work machine 40 being affected by a disaster in a second designated area including an existence position of the work machine 40 is predicted based on an amount of rainfall in a first designated area. A hazard map representing a result of the prediction of the possibility of the work machine 40 being affected by a disaster in the second designated area is outputted to a remote output interface 220 in a remote operation apparatus 20 (a client) (or a management output interface 620 in a management client 60). Accordingly, a user can take measures to reduce the possibility of the work machine being affected by a disaster, for example, to communicate with the persons involved in order to move the work machine 40 from a current position.

Embodiments described herein relate to predicting the utilization of electric vehicle (EV) charge points. Methods may include: receiving an indication of a plurality of candidate locations for EV charge points; determining static map features of the plurality of candidate locations; inputting the plurality of candidate locations and static map features into a machine learning model, where the machine learning model is trained on existing EV charge point locations, existing EV charge point static map features, and existing EV charge point utilization; determining, based on the machine learning model, a predicted utilization of an EV charge point at the plurality of candidate locations; and generating a representation of a map including the plurality of candidate locations, where candidate locations of the plurality of candidate locations are visually distinguished based on a respective predicted utilization of an EV charge point at the candidate locations.

In an approach to jointly learning uncertainty-aware trend-informed neural network for a demand forecasting model, a machine learning model is trained to capture uncertainty in input forecasts. The uncertainty in a latent space is represented using an auto-encoder based neural architecture. The uncertainty-aware latent space is modeled and optimized to generate an embedding space. A time-series regressor model is learned from the embedding space. A machine learning model is trained for trend-aware demand forecasting based on said time-series regressor model.

A method includes obtaining an optimized setpoint schedule for a time period, identifying a pre-cooling or pre-heating segment of the time period of the optimized setpoint schedule, adjusting the optimized setpoint schedule based on a characteristic of the pre-cooling or pre-heating segment to obtain an adjusted setpoint schedule, and operating the building equipment in accordance with the adjusted setpoint schedule.

A method can be used to predict risk using machine learning models having efficient feature learning. A risk prediction model can be applied to time-series data associated with a target entity to generate a risk indicator. The risk prediction model can include a feature learning model for generating features from the time-series data. The risk prediction model can also include a risk classification model for generating the risk indicator. The feature learning model can include filters and can be trained. Parameters of the risk prediction model can be adjusted to minimize a loss function associated with risk indicators. An updated risk prediction model can be generated by removing a filter from an original set of filters based on influencing scores of the original filters. The risk indicator can be transmitted to a computing device for use in controlling access of the target entity to a computing environment.

The present disclosure relates to methods and systems for determining an estimated time of arrival (ETA). The methods may include obtaining feature data related to an on-demand service order; obtain a parallel computing framework; determining a global ETA model based on the feature data and the parallel computing framework; and determining an ETA for a target route based on the global ETA model.

A power management system obtains first data regarding several groups of electrical devices, including a budget and a respective priority metric associated with each of the groups. The system allots a respective amount of electrical power for use by each of the groups based on the first data. Further, the system obtains second data regarding the groups, including a respective amount of electrical power consumed by each of the groups. The system determines that a first group has consumed a first amount of electrical power that is greater than or equal to a second amount of electrical power that had been allotted for use by the first group. In response, the system re-allots at least a portion of a third amount of electrical power that had been allotted for use by a second group for use by the first group instead.

A power management system obtains first data regarding several groups of electrical devices, including a budget and a respective priority metric associated with each of the groups. The system allots a respective amount of electrical power for use by each of the groups based on the first data. Further, the system obtains second data regarding the groups, including a respective amount of electrical power consumed by each of the groups. The system determines that a first group has consumed a first amount of electrical power that is greater than or equal to a second amount of electrical power that had been allotted for use by the first group. In response, the system re-allots at least a portion of a third amount of electrical power that had been allotted for use by a second group for use by the first group instead.

A contactless locker system includes a set of laterally adjacent lockers that includes a first locker configured to store a first food item prepared by a restaurant. The first locker includes a first door that automatically opens and closes without a user physically touching the first door and a second door that automatically opens and closes without a user physically touching the second door.

A contactless locker system includes a set of laterally adjacent lockers that includes a first locker configured to store a first food item prepared by a restaurant. The first locker includes a first door that automatically opens and closes without a user physically touching the first door and a second door that automatically opens and closes without a user physically touching the second door.