Abstract: A method of coupon distribution is used in connection with a demand response (DR) event. The method includes clustering DR customers into customer clusters based on energy use behaviors of the DR customers. Suggested coupons are received from merchants, each coupon including load serving entity (LSE) and merchant contributions. Based on energy price forecast, the suggested coupons, and customer information, a coupon distribution is found to maximizes a financial benefit to the LSE and includes an optimal number of the suggested coupons to be distributed to the customer clusters. The suggested coupons are distributed to the customer clusters per the coupon distribution and collecting responses to the suggested coupons indicating participation. Based on the customer responses, the method includes estimating energy curtailment contributions of the DR customers and an actual energy price for the DR event and communicating to an independent system operator an energy transaction bid based thereon.

Abstract: A method of forecasting a resource load for consumption at a site, e.g., an electrical load of a site. The method includes receiving historical load data and historical ambient condition data that are time series data pertaining to a site and generating additional data from the received data. The method includes building a best sub-model for each of multiple forecast intervals. The building includes clustering in parallel training portions of the historical load data and the additional data, training possible sub-models using the clustered training portions, verifying forecasted loads output from the possible sub-models against verification portions of the historical load data and the additional data, and determining a first subset of parameters for the best sub-model based upon accuracy of the forecasted loads. The method includes forecasting a resource load at the site for each of the forecast intervals using an ensemble of the best sub-models.

Abstract: A method of coupon distribution is used in connection with a demand response (DR) event. The method includes clustering DR customers into customer clusters based on energy use behaviors of the DR customers. Suggested coupons are received from merchants, each coupon including load serving entity (LSE) and merchant contributions. Based on energy price forecast, the suggested coupons, and customer information, a coupon distribution is found to maximizes a financial benefit to the LSE and includes an optimal number of the suggested coupons to be distributed to the customer clusters. The suggested coupons are distributed to the customer clusters per the coupon distribution and collecting responses to the suggested coupons indicating participation. Based on the customer responses, the method includes estimating energy curtailment contributions of the DR customers and an actual energy price for the DR event and communicating to an independent system operator an energy transaction bid based thereon.

Abstract: Some embodiments determine, for electric vehicles within an electricity distribution network, optimal recharging schedules based on customer requirements. Some embodiments adjust the optimal recharging schedules to ensure that components of the electricity distribution network operate within their rated limits based on a node sensitivity approach.

Abstract: A method of forecasting a resource load for consumption at a site, e.g., an electrical load of a site. The method includes receiving historical load data and historical ambient condition data that are time series data pertaining to a site and generating additional data from the received data. The method includes building a best sub-model for each of multiple forecast intervals. The building includes clustering in parallel training portions of the historical load data and the additional data, training possible sub-models using the clustered training portions, verifying forecasted loads output from the possible sub-models against verification portions of the historical load data and the additional data, and determining a first subset of parameters for the best sub-model based upon accuracy of the forecasted loads. The method includes forecasting a resource load at the site for each of the forecast intervals using an ensemble of the best sub-models.

Abstract: Some embodiments determine, for electric vehicles within an electricity distribution network, optimal recharging schedules based on customer requirements. Some embodiments adjust the optimal recharging schedules to ensure that components of the electricity distribution network operate within their rated limits based on a node sensitivity approach.