Abstract: Distributed energy trading settlement is provided. The method comprises polling a number of Internet-of-Things (IoT) devices for energy consumption and production data for a specified preceding time interval and receiving weather data from a weather application programming interface (API) for the same time interval. A number of bids and offers are received from a central modelling solution within a specified trading window, based on the data from the IoT devices and weather API. Orders are then created from the bids and offers. At least a subset of the orders are matched to create a number of matched trades, which are published to a message queue. The published matched trades are committed as transactions in a blockchain, and tokens are transferred between user wallets according to the transactions in the blockchain.

Abstract: In some embodiments, apparatuses and methods are provided herein useful to providing personalized footwear and/or generating personalized footwear recommendations. In some embodiments, systems and methods for providing personalized footwear may include or employ one or more of a foot shape module, a pathology module, a gait and biomechanics module, and a context module. The foot shape, pathology, gait, biomechanics, and context modules may acquire and analyze data related to foot shape, pathologies, gait, biomechanics, demographics, lifestyle, location, and/or product preferences associated with a user to determine one or more personal attributes associated with the user. In addition, the systems and methods provided herein may include or employ a footwear module to collect and analyze data related to construction, materials, material properties, category, style, and/or aesthetics of footwear products to determine footwear attributes associated with the products.

Abstract: A method of trading electrical energy is provided. The method comprises a smart agent receiving state data affecting electricity usage within an electrical power grid over a specified time period and forecasting, with a supply/demand model, supply and demand for electricity within the power grid according to the state data. The smart agent uses a reinforced learning neural network to calculate a price for electricity according the state data and forecasted supply and demand. The smart agent submits an order to a matching engine to buy or sell electricity on the power grid at the calculated price according to specified market rules. The smart engine receives an acknowledgment from the matching engine if the order is matched to another agent on the power grid or a rejection from the matching engine if the order is not matched to another agent.

Abstract: A method of trading electrical energy is provided. The method comprises a smart agent receiving state data affecting electricity usage within an electrical power grid over a specified time period and forecasting, with a supply/demand model, supply and demand for electricity within the power grid according to the state data. The smart agent uses a reinforced learning neural network to calculate a price for electricity according the state data and forecasted supply and demand. The smart agent submits an order to a matching engine to buy or sell electricity on the power grid at the calculated price according to specified market rules. The smart engine receives an acknowledgment from the matching engine if the order is matched to another agent on the power grid or a rejection from the matching engine if the order is not matched to another agent.

Abstract: Distributed energy trading settlement is provided. The method comprises polling a number of Internet-of-Things (IoT) devices for energy consumption and production data for a specified preceding time interval and receiving weather data from a weather application programming interface (API) for the same time interval. A number of bids and offers are received from a central modelling solution within a specified trading window, based on the data from the IoT devices and weather API. Orders are then created from the bids and offers. At least a subset of the orders are matched to create a number of matched trades, which are published to a message queue. The published matched trades are committed as transactions in a blockchain, and tokens are transferred between user wallets according to the transactions in the blockchain.

Abstract: A method of trading electrical energy is provided. The method comprises a smart agent receiving state data affecting electricity usage within an electrical power grid over a specified time period and forecasting, with a supply/demand model, supply and demand for electricity within the power grid according to the state data. The smart agent uses a reinforced learning neural network to calculate a price for electricity according the state data and forecasted supply and demand. The smart agent submits an order to a matching engine to buy or sell electricity on the power grid at the calculated price according to specified market rules. The smart engine receives an acknowledgment from the matching engine if the order is matched to another agent on the power grid or a rejection from the matching engine if the order is not matched to another agent.