Abstract: A method including generating a mapping in response to an enrollment message received from a customer device. The enrollment message includes a device identification number (device ID) of the customer device. The mapping includes a one-time password (OTP) encrypted using the device ID. The method includes generating a quick response (QR) code including a uniform resource locator (URL), a provider certificate (CRT), and certificate signing request (CSR) content. The method includes receiving a confirmation message at a website identified by the URL. The confirmation message includes a generated public key, a CSR, and a decrypted OTP. The method includes verifying the decrypted OTP against the mapping and communicating one or more application configuration settings. The method includes securely communicating information using the one or more application configuration settings, a provider public key, a provider private key, the generated public key, and a generated private key.

Abstract: A method of residential or small and medium business (SMB) demand response (DR) coordination may include receiving a DR event notification from a DR server. The DR event notification may include DR event information pertaining to a DR event and curtailment information. The method may also include communicating a notification acknowledgement to the DR server. The notification acknowledgement may indicate reception of the DR event notification. The method may include displaying a curtailment request pertaining to the DR event. The curtailment request may be based on the curtailment information included in the DR event notification and may include some portion of the DR event information. The method may also include receiving a user input including a DR event participation confirmation or a DR event participation refusal. The method may further include communicating a participation signal representative of at least a portion of the user input to the DR server.

Abstract: A method may include receiving data and first path-metadata. The first path-metadata may include a first entity identifier. The first entity identifier may be associated with a first receiving entity that receives the data and the first path-metadata from an originating entity. The first path-metadata may also include a first digital signature associated with the originating entity. The method may further include receiving second path-metadata that may include the first path-metadata and a second entity identifier associated with a second receiving entity. The second path-metadata may also include a second digital signature associated with the first receiving entity. The method may additionally include verifying that the data was communicated by the originating entity to the first receiving entity and from the first receiving entity to the second receiving entity based on the first path-metadata, the second path-metadata, the first digital signature, and the second digital signature.

Abstract: A method including receiving energy usage data representative of energy usage of a customer during a particular time period. The energy usage data is sign with a digital signature of a utility. The method includes receiving input of a customer effective to select a data block of the energy usage data. The method includes redacting the selected data block from the energy usage data in response to the input. The method includes calculating a hash value for the redacted data block using a per-customer key that is unique to the customer, an initialization vector, and a counter. The method includes replacing in the energy usage data the redacted data block with the calculated hash value corresponding to the redacted data block.

Abstract: A method may include receiving peer data describing a set of peer clients associated with a demand response application server and describing how the peer clients communicate with one another. The peer data may be configured so that a subset of the peer clients directly communicate with the demand response application server and the demand response application server does not directly communicate with each of the peer clients. The method may also include receiving announcement data describing an event specified by the demand response application server and determining event response data responsive to the announcement data. The method may also include identifying, from the set of peer clients specified by the demand response application server, a set of recipient peer clients to receive the event response data.

Abstract: A method may include receiving data and first path-metadata. The first path-metadata may include a first entity identifier. The first entity identifier may be associated with a first receiving entity that receives the data and the first path-metadata from an originating entity. The first path-metadata may also include a first digital signature associated with the originating entity. The method may further include receiving second path-metadata that may include the first path-metadata and a second entity identifier associated with a second receiving entity. The second path-metadata may also include a second digital signature associated with the first receiving entity. The method may additionally include verifying that the data was communicated by the originating entity to the first receiving entity and from the first receiving entity to the second receiving entity based on the first path-metadata, the second path-metadata, the first digital signature, and the second digital signature.

Abstract: A method of sharing energy usage data may include requesting customer-specific energy usage data (CSEUD). The method further includes receiving a modified CSEUD record. The modified CSEUD record includes modified energy usage data with respect to original energy usage data of an original CSEUD record from which the modified CSEUD record is derived. The method also includes authenticating the modified CSEUD record to verify the integrity of the modified energy usage data.

Abstract: Embodiments for detecting anomalous consumption of energy are provided. Information associated with energy consumption over a designated period of time is received. A threshold value is received. A classifier based on an Auto-Regressive Moving Average model is applied to the information and a result representing the likelihood of an attack is determined. The result is then analyzed to determine if it attained a threshold value. The information is then classified as indicating an attack. Additionally, embodiments for utilizing machine learning to train a classifier using training data to develop parameters for the auto-regressive moving average model are provided. Further, embodiments for evaluating the effectiveness of the parameters used in the Auto-Regressive Moving Average model to classify data are provided.