A method and a system of predicting an electric system load based on wavelet noise reduction and empirical mode decomposition-autoregressive integrated moving average (EMD-ARIMA) are provided. The method and the system belong to a field of electric system load prediction. The method includes the following steps. Raw load data of an electric system is obtained first. Next, noise reduction processing is performed on the load data through wavelet analysis. The noise-reduced load data is further processed through an EMD method to obtain different load components. Finally, ARIMA models corresponding to the different load components are built. Further, the ARIMA models are optimized through an Akaike information criterion (AIC) and a Bayesian information criterion (BIC). The load components obtained through predicting the different ARIMA models are reconstructed to obtain a final prediction result, and accuracy of load prediction is therefore effectively improved.

A trading planning apparatus includes an order quantity planning section determining trading quantities with a plurality of trade connections from a trading cumulative quantity and estimated data about future quantities related to demand and the trade connections, a split-time-based split order planning section generating trading and ordering data containing data about an order price or an order quantity related to trading and ordering in each of planned trading periods that are periods into which a trading period, during which trading can be conducted, is subdivided, and a future quantity estimation section estimating estimated quantities of data related to errors in the future quantities estimated from actual record values, the order quantity planning section increasing or reducing the trading quantity of any of the trade connections when a difference between a sum of the trading quantities of all of the trade connections and a future quantity demanded is equal to or greater than a predetermined value.

A system for allocating one or more resources including electrical energy across equipment that operate to satisfy a resource demand of a building. The system includes electrical energy storage including one or more batteries configured to store electrical energy purchased from a utility and to discharge the stored electrical energy. The system further includes a controller configured to determine an allocation of the one or more resources by performing an optimization of a value function. The value function includes a monetized cost of capacity loss for the electrical energy storage predicted to result from battery degradation due to a potential allocation of the one or more resources. The controller is further configured to use the allocation of the one or more resources to operate the electrical energy storage.

A method of estimating a parameter relating to an upgrade of a wind turbine software includes toggling the wind turbine alternatingly between two modes, where the upgrade is implemented in one but not the other. Data is collected during toggling and divided into pairs of data parts, one from each mode. The data from the two modes is ordered separately and a quantile-to-quantile comparison is made.

A personal power plant stores energy at the load site from renewable sources and through connection with a utility, enabling the personal power plant to support real time or near real time system load balancing and to eliminate net-metering. The personal power plant determines a charging profile based on predicted energy usage for a next 24-hour period. The personal power plant can provide an indication of the excess amount of electrical energy stored, but not needed for the next 24-hour period. When times of high demand are anticipated, the utility can reallocate energy to other users which it would otherwise provide to participating personal power plants aggregating the excess energy from a plurality of personal power plants. At such times, inverse power generation occurs as the personal power plants do not draw power from the grid which, in aggregation, reduces grid load.

A building energy system for a building includes an energy storage system (ESS) configured to store energy received from an energy source and provide the stored energy to one or more pieces of building equipment to operate the one or more pieces of building equipment. The system includes a processing circuit configured to collect building data, determine whether to retrain a trained load prediction model based on at least some of the building data, retrain the trained load prediction model based on the building data in response to a determination to retrain the trained load prediction model, determine a load prediction for the building based on the retrained load prediction model, and operate the ESS to store the energy received from the energy source or provide the stored energy to the one or more pieces of building equipment to operate the one or more pieces of building equipment.

According to embodiments of the present invention, techniques are provided for producing energy from one or more energy production sources. Energy consumption and energy production sources for an entity in a geographic location are analyzed. Sensor data based on the analysis of the entity is collected, wherein the sensor data includes crowdsourced sensor data. A model is generated based on the collected sensor data and the analysis of the entity, wherein the model includes optimization criteria for energy production. The model is utilized to determine an energy production source and energy production for the entity. Occurrence of a triggering event is detected, and the energy production source is adjusted based on the detected triggering event. The model is updated based on newly collected sensor data.

A grid distribution system aggregates energy resources of multiple distributed energy resources (DERs) and provides service to one or more energy markets with the DERs as a single market resource. The DERs can create data to indicate realtime local demand and local energy capacity of the DERs. Based on DER information and realtime market information, the system can compute how to provide one or more services to the power grid based on an aggregation of DER energy capacity.

A power grid management system for a power grid to which multiple electrical vehicles and multiple charging points are connected, each charging point including a power supply and a power interface connecting the power supply to an electrical vehicle; the system comprising a load management module that dynamically manages the load on the power grid by taking into account each individual electrical vehicle's characteristics and/or each respective, connected individual charging point's characteristics.

A reference capacity determiner determines a reference capacity of each power storage apparatus, under an assumption that power is not transmitted/received among the customer facilities. An actual capacity determiner determines an actual capacity of each power storage apparatus, under an assumption that power is transmitted/received among the customer facilities. A characteristic value determiner determines a first number of charging/discharging cycles, under an assumption that power is not transmitted/received among the customer facilities and each power storage apparatus has the reference capacity. A charging/discharging plan determiner determines a second number of charging/discharging cycles, under an assumption that power is transmitted/received among the customer facilities and each power storage apparatus has the actual capacity, and determine a charging/discharging plan of the power storage apparatuses so as to minimize an objective function indicating a degree of degradation from the first number of cycles to the second number of cycles.