The invention relates to a method for predicting benchmark value of unit equipment based on XGBoost algorithm and a system thereof, wherein the method comprises the following steps: the historical operation data of unit equipment is obtained, the data is preprocessed, and a data set containing a plurality of samples is constructed, and each sample includes the benchmark value of a plurality of parameters of the equipment corresponding to a plurality of features; RF out-of-bag estimation is used for feature importance calculation to eliminate the features with low importance; the data is standardized to eliminate the dimensional effects among features; the data set is input to construct an XGBoost model, and Bayesian super parameter optimization is conducted to obtain the prediction model of benchmark values; and the real-time data of equipment operation is input, and the benchmark values of various equipment parameters are predicted by the prediction model of benchmark values. Compared with the prior art, the invention mines the correlation among data based on the XGBoost algorithm to predict a reasonable equipment benchmark value, and has the advantages of high generalization ability, high prediction accuracy and operation speed and great improvement of the automation ability of the unit.

Systems and methods for controlling power flow to and from an energy storage system are provided. One implementation relates to an energy storage system comprising an energy storage device, an inverter configured to control a flow of power out of the energy storage device, a rectifier configured to control the flow of power into the energy storage device and one or more controllers. The one or more controllers may be configured to determine a schedule of a plurality of time periods based on historical price data. Each of the plurality of time periods may be associated with one of a state of charging, discharging, or idle. The one or more controllers may be configured to control the inverter and the rectifier based on the determined schedule.

Techniques for benchmarking performance in a contact center system are disclosed. In one particular embodiment, the techniques may be realized as a method for benchmarking contact center system performance comprising cycling, by at least one computer processor configured to perform contact center operations, between a first contact-agent pairing strategy and a second contact-agent pairing strategy for pairing contacts with agents in the contact center system; determining an agent-utilization bias in the first contact-agent pairing strategy comprising a difference between a first agent utilization of the first contact-agent pairing strategy and a balanced agent utilization; and determining a relative performance of the second contact-agent pairing strategy compared to the first contact-agent pairing strategy based on the agent-utilization bias in the first contact-agent pairing strategy.

Technologies for allocating resources of managed nodes to workloads to balance multiple resource allocation objectives include an orchestrator server to receive resource allocation objective data indicative of multiple resource allocation objectives to be satisfied. The orchestrator server is additionally to determine an initial assignment of a set of workloads among the managed nodes and receive telemetry data from the managed nodes. The orchestrator server is further to determine, as a function of the telemetry data and the resource allocation objective data, an adjustment to the assignment of the workloads to increase an achievement of at least one of the resource allocation objectives without decreasing an achievement of another of the resource allocation objectives, and apply the adjustments to the assignments of the workloads among the managed nodes as the workloads are performed. Other embodiments are also described and claimed.

The accuracy of photovoltaic simulation modeling is predicated upon the selection of a type of solar resource data appropriate to the form of simulation desired. Photovoltaic power simulation requires irradiance data. Photovoltaic energy simulation requires normalized irradiation data. Normalized irradiation is not always available, such as in photovoltaic plant installations where only point measurements of irradiance are sporadically collected or even entirely absent. Normalized irradiation can be estimated through several methodologies, including assuming that normalized irradiation simply equals irradiance, directly estimating normalized irradiation, applying linear interpolation to irradiance, applying linear interpolation to clearness index values, and empirically deriving irradiance weights. The normalized irradiation can then be used to forecast photovoltaic fleet energy production.

A system and method for energy distribution leveraging dynamic feedforward allocation of distributed energy storage using multiple energy distribution pathways to maximize load-balancing to accelerate return on investment, reduce system energy consumption, and maximize utilization of existing energy infrastructure particularly for modular construction.

A photovoltaic system can include multiple photovoltaic power inverters that convert sunlight to power. An amount of power for each of the inverters can be measured over a period of time. These measurements, along with other data, can be collected. The collected measurements can be used to generate artificial neural networks that predict the output of each inverter based on input parameters. Using these neural networks, the total solar power generation forecast for the photovoltaic system can be predicted.

A system includes a processor and a non-transitory, computer-readable memory that includes instructions executable by the processor for causing the processor to perform operations. The operations include receiving a query including an asset inventory or maintenance question of an asset of a power distribution network. The operations further include accessing data of the power distribution network that is associated with the asset and applying a forecasting model to the query and the data of the power distribution network that is associated with the asset to generate an asset inventory or maintenance forecast. Additionally, the operations include controlling an ordering operation of the asset or a maintenance scheduling of the asset using the asset inventory or maintenance forecast.

The present disclosure is directed to systems and methods for economically optimal control of an electrical system. A two-stage controller includes an optimizer and a high speed controller to effectuate a change to one or more components of the electrical system. The high speed controller receives a set of control parameters for an upcoming extended time period. The control parameters include a plurality of bounds for an adjusted net power of the electrical system. The high speed controller sets an energy storage system command control variable (ESS command) based on a state of adjusted net power of the electrical system and the set of control parameters.

There is described a method of reserving a capacity of one or more energy storage devices. The method includes forecasting, based on past electricity demand of a site, future electricity demand of the site over a future time period. The method further includes determining a forecasting error between the forecasted future electricity demand and an actual electricity demand of the site over the future time period. The method further includes adjusting, based on the forecasting error, a target state of charge (SOC) of one or more energy storage devices. The method further includes reserving, based on the adjusted target SOC, a capacity of the one or more energy storage devices.