A method for the optimized use of a parking area. Vehicles which are to be parked on the parking area are each assigned a parking space, the vehicles navigating to the respective assigned parking space, in particular autonomously. Furthermore, vehicles may carry out a change of the parking space in order to enable an improved use of the available parking area or a faster availability of the vehicle, whereby overall an optimized use of the parking area is achieved. Initially, an available range of the respective vehicles is ascertained, and the assignment of the respective parking space and/or a possible change of the parking space are made dependent on the available range of the individual vehicles. The available range of a vehicle is in particular described by a residual fuel amount or a charge state of the vehicle.

Provided is a system for optimizing utilization of a population of underutilized physical facilities and including a time interval splitting controller operative to communicate with some but not all of a plurality of computerized organizations, the time interval splitting controller being configured to perform certain operations when a computerized organization, from among some but not all of the plurality of computerized organizations, seeks to reserve at least one portion of at least one physical facility for a specific time interval, including using a processor for partitioning at least one specific time interval at least twice, thereby to define at least first and second partitions thereof, each partition including a sequence of sub-intervals each having a length which does not exceed the specific time interval's length.

A building management system includes a building efficiency improvement system and method configured to monitor and control subsystems and equipment for improved efficiency of operation. A user device is configured to display a user interface for monitoring and controlling one or more building equipment efficiency parameters and settings. The building efficiency management system further includes a controller configured to collect and analyze data from equipment, generate displays of the operational status and efficiency levels, generate sets of alternative equipment control algorithms based on efficiency objectives, and present users with a set of alternative equipment control algorithms displayed via graphic user interface elements on the user device. The user device further provides a means to select and implement an alternate equipment control algorithm. The controller is further configured to receive inputs from the user device commanding changes to equipment controls and process transactions associated with changes to equipment configuration.

Methods, systems, and non-transitory computer readable storage media are disclosed for generating action recommendations for generating action recommendations for modifying physical emissions sources of an entity based on forecasting and monitoring emissions production for the entity utilizing machine-learning models. Specifically, the disclosed system forecasts emissions produced by an entity by utilizing a plurality of different forecasting machine-learning models corresponding to different physical emissions sources to generate forecasted source attributes. Additionally, the disclosed system combines the forecasted source attributes to generate a plurality of forecasted emissions value modifications for a future time period. The disclosed system generates action recommendations for modifying the physical emissions sources based on the forecasted emissions value modifications. In additional embodiments, the disclosed system tracks emissions of the entity during the future time period and generate additional action recommendations in response to detecting deviations from forecasted emissions production.

Methods, systems, and non-transitory computer readable storage media are disclosed for generating action recommendations for generating action recommendations for modifying physical emissions sources of an entity based on forecasting and monitoring emissions production for the entity utilizing machine-learning models. Specifically, the disclosed system forecasts emissions produced by an entity by utilizing a plurality of different forecasting machine-learning models corresponding to different physical emissions sources to generate forecasted source attributes. Additionally, the disclosed system combines the forecasted source attributes to generate a plurality of forecasted emissions value modifications for a future time period. The disclosed system generates action recommendations for modifying the physical emissions sources based on the forecasted emissions value modifications. In additional embodiments, the disclosed system tracks emissions of the entity during the future time period and generate additional action recommendations in response to detecting deviations from forecasted emissions production.

The present disclosure relates to a system and a method of allocating orders. The system may include a non-transitory computer readable storage medium and a processor. The non-transitory computer may store an executable module. The processor may execute the executable module stored in the computer readable storage medium. The non-transitory computer readable storage medium may include a receiving unit (231) and an order allocation unit (361). The receiving unit (231) may receive order information and user information. The user information may include location information and/or time information. The order allocation unit (361) may allocate an order based on the location information and/or the time information. The method may include receiving order information and user information, wherein the order information and the user information may include location information and/or time information, and allocating an order based on the location information and/or the time information.

The present disclosure relates to a system and a method of allocating orders. The system may include a non-transitory computer readable storage medium and a processor. The non-transitory computer may store an executable module. The processor may execute the executable module stored in the computer readable storage medium. The non-transitory computer readable storage medium may include a receiving unit (231) and an order allocation unit (361). The receiving unit (231) may receive order information and user information. The user information may include location information and/or time information. The order allocation unit (361) may allocate an order based on the location information and/or the time information. The method may include receiving order information and user information, wherein the order information and the user information may include location information and/or time information, and allocating an order based on the location information and/or the time information.

A device may receive equipment information, associated with a first equipment, including information associated with anomalies identified based on operational information collected during operation of the first equipment, and messages generated during the operation of the first equipment. The device may receive maintenance information, associated with the first equipment, that identifies one or more part failures associated with one or more equipment parts. The device may identify associations between the one or more part failures and the first equipment information. The device may receive equipment information, associated with a second equipment, including information associated with anomalies identified based on operational information collected during operation of the second equipment, and messages generated during the operation of the second equipment. The device may generate and provide a prediction, associated with a future failure of an equipment part of the second equipment, based on the second equipment information and the associations.

A facility providing systems and methods for managing and optimizing energy consumption and/or production is provided. The facility provides techniques for optimizing energy-consuming and energy-producing systems to meet specified demands or goals in accordance with various constraints. The facility relies on models to generate an optimization for an energy system. In order to use generic models to simulate and optimize energy consumption for an energy system, the generic models are calibrated to properly represent or approximate conditions of the energy system during the optimization period. After the appropriate models have been calibrated for a given situation using one or more modeling parameter sets, the facility can simulate inputs and responses for the corresponding system. The facility uses the generated simulations to generate a plan or control schedule to be implemented by the energy system during the optimization period.

A method for generating a trip prediction specific to a given user includes acquiring a first dataset of trip histories taken in a given transportation network; dividing a trip history of a given user at a specific time point into user training and validation datasets; acquiring training datasets each associated with candidate neighboring users; identifying useful neighbors from the training and validation datasets; combining the user trip history and the trip history of each useful neighbor; applying a similarity function to the combined dataset, wherein a sum of similarities between a given trip and all other trips in the combined dataset is computed; associating a trip having the highest weighted similarity (weighted by frequency) with a prediction for a future trip; and outputting the prediction to an associated user device.