A multi-site Building Management System (BMS) monitors performance of a local BMS at each of a plurality of remote sites. The multi-site BMS includes a controller that is configured to determine a plurality of local performance metrics associated with each local BMS based on the operational data received from each local BMS and to aggregate like ones of the plurality of local performance metrics, resulting in a plurality of aggregated performance metrics. The controller is configured to display on the display a plurality of panels, to display in each of the plurality of panels the corresponding one of the plurality of aggregated performance metrics and to display in each of the plurality of panels a ranking of one or more of the remote sites by their corresponding local performance metric.

A maintenance management method for a lithography system according to a viewpoint of the present disclosure includes organizing and saving operating information for each of lithography cells that are each an apparatus group formed of a set of apparatuses and form the lithography system, organizing and saving maintenance information on consumables for each of the lithography cells, calculating a standard maintenance timing for each of the consumables for each of the lithography cells based on the operating information and the maintenance information on the consumable for each of the lithography cells, creating a maintenance schedule plan for each of the lithography cells or for each of manufacturing lines based on the standard maintenance timing, information on a downtime, and information on a loss cost due to the downtime for each of the lithography cells or for each of the manufacturing lines, and outputting the result of the creation of the maintenance schedule plan.

A system with an energy consumption data unit configured to determine an energy consumption data associated with an entity in different cycles. A structural drift identifier unit is configured to classify the energy consumption data into multiple individual drift classes. A non-linear signal extractor unit is configured to extract a set of non-linear signals from the energy consumption data by defining cycles based on the frequency of energy consumption data and evaluating the variance in energy consumption data. The system includes an individual drift class refinement unit configured to refine individual drift classes by eliminating first weak drifts using the set of non-linear signals resulting into refined individual drift classes. A collective drift refinement unit configured to collate and collectively refine individual drift instances of all the refined individual drift classes into refined collective drift classes by eliminating second weak drifts.

A method for providing a planning canvas may include receiving a request for a planning canvas, defining one or more group priorities for the planning canvas for one or more time periods based at least in part on one or more received user interactions, receiving a confirmation to release the planning canvas, and in response to receiving the confirmation to release the planning canvas, enabling access to the one or more group priorities for one or more associated users.

A method and system generate a building operational performance analysis output. A synthetic data set is generated and includes a set of 3D building conceptual mass geometries. The generating includes identifying geometry types, dividing the geometry types into categories, and algorithmically generating the mass geometries using a separate workflow for each category using generative design. Analytical models associated with each of the mass geometries are generated. Simulation results are generated for each of the analytical models. A surrogate model is trained based on a set of features extracted from the simulation results using machine learning (ML). The ML iteratively determines the set of features based on a measured accuracy of the surrogate model. Geometry input is received and processed through the surrogate model to generate the building operational performance analysis output which is then used to inform a designer of an approximate Energy Use Intensity of the geometry input.

A method for providing an energy map may include defining group priorities, receiving indication of status for each of a plurality of individual entities with respect to corresponding priorities defined for each respective individual entity, correlating received indications of status to respective group priorities, providing a representation of group priorities, and mapping an amount of energy associated with the group priorities or individual priorities by providing a graphical representation of a respective amount of resources based on the received indications.

Processing of data relating to energy usage. First data relating to energy usage is loaded for analysis by an energy management platform. Second data relating to energy usage is stream processed by the energy management platform. Third data relating to energy usage is batch parallel processed by the energy management platform. Additional computing resources, owned by a third party separate from an entity that owns the computer system that supports the energy management platform, are provisioned based on increasing computing demand. Existing computing resources owned by the third party are released based on decreasing computing demand.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining causal models for controlling environments. One of the methods includes obtaining data specifying baseline probability distributions for each of a plurality of controllable elements; maintaining a causal model; repeatedly performing the following: selecting control settings for the environment based on the causal model and values for a particular internal parameter of the control system that are sampled from a range of possible values; selecting control settings for the environment based on the baseline probability distributions; monitoring environment responses to the control settings selected based on the causal model and the control settings selected based on the baseline probability distributions; determining, for each of the possible values, a measure of a difference between a current system performance and a baseline system performance; and updating how frequently each of the possible values is sampled.

A system and methods for multivariant learning and optimization repeatedly generate self-organized experimental units (SOEUs) based on the one or more assumptions for a randomized multivariate comparison of process decisions to be provided to users of a system. The SOEUs are injected into the system to generate quantified inferences about the process decisions. Responsive to injecting the SOEUs, at least one confidence interval is identified within the quantified inferences, and the SOEUs are iteratively modified based on the at least one confidence interval to identify at least one causal interaction of the process decisions within the system. The causal interaction can be used for testing, diagnosis, and optimization of the system performance.

An evaluation value prediction unit predicts an evaluation value at a time point after a given time passes from a predetermined evaluation time based on input data related to an operation of a factory at the evaluation time using a learned prediction model. The prediction model is a learned model that is learned so that the evaluation value related to the operation of the factory at a time point after the given time passes from one time point is output by inputting a plurality of kinds of data related to the operation of the factory at one time point. An evaluation value output unit outputs information related to the evaluation value.