Abstract: Provided is a method for relocating a solar power unit in response to a redeployment event. A first location of a deployed solar power unit may be determined. A processor may detect a redeployment event for the solar power unit at the first location. In response to the redeployment event, the processor may determine a new location for the solar power unit. The method may further comprise relocating the solar power unit to the new location.

Abstract: Provided is a method for deploying multiple solar power units to a location. The method comprises determining a location to which a solar power unit is deploying. The method further comprises determining that a second solar power unit is deploying to a same location as the solar power unit. A redeployment plan for the second solar power unit is generated. The redeployment plan is provided to the second solar power unit. The method further comprising deploying the solar power unit to the location.

Abstract: Provided is a method for relocating a solar power unit in response to a redeployment event. A first location of a deployed solar power unit may be determined. A processor may detect a redeployment event for the solar power unit at the first location. In response to the redeployment event, the processor may determine a new location for the solar power unit. The method may further comprise relocating the solar power unit to the new location.

Abstract: Provided is a method for deploying multiple solar power units to a location. The method comprises determining a location to which a solar power unit is deploying. The method further comprises determining that a second solar power unit is deploying to a same location as the solar power unit. A redeployment plan for the second solar power unit is generated. The redeployment plan is provided to the second solar power unit. The method further comprising deploying the solar power unit to the location.

Abstract: An approach is provided for using a machine learning model to reduce food waste. Estimation models for food items are created by using a machine learning model. The estimation models have prediction functions specifying timelines during which the food items are not spoiled and are in a condition for consumption. Prediction function solutions are generated for a food item inventory to indicate menu items for respective time periods. Each menu item includes food item(s) which are in condition for consumption based on the estimation models. Menu recommendations corresponding to the time periods are generated. Each menu includes menu item(s) indicated by the prediction function solutions. The menu recommendations reduce a waste of the food items through spoilage by increasing a likelihood of consumption of the food items within the respective timelines.

Abstract: Provided is a method for relocating a solar power unit in response to a redeployment event. A first location of a deployed solar power unit may be determined. A processor may detect a redeployment event for the solar power unit at the first location. In response to the redeployment event, the processor may determine a new location for the solar power unit. The method may further comprise relocating the solar power unit to the new location.

Abstract: Provided is a method for deploying multiple solar power units to a location. The method comprises determining a location to which a solar power unit is deploying. The method further comprises determining that a second solar power unit is deploying to a same location as the solar power unit. A redeployment plan for the second solar power unit is generated. The redeployment plan is provided to the second solar power unit. The method further comprising deploying the solar power unit to the location.

Abstract: Provided is a method for relocating a solar power unit in response to a redeployment event. A first location of a deployed solar power unit may be determined. A processor may detect a redeployment event for the solar power unit at the first location. In response to the redeployment event, the processor may determine a new location for the solar power unit. The method may further comprise relocating the solar power unit to the new location.

Abstract: Provided is a method for deploying multiple solar power units to a location. The method comprises determining a location to which a solar power unit is deploying. The method further comprises determining that a second solar power unit is deploying to a same location as the solar power unit. A redeployment plan for the second solar power unit is generated. The redeployment plan is provided to the second solar power unit. The method further comprising deploying the solar power unit to the location.

Abstract: An approach is provided for using a machine learning model to reduce food waste. Estimation models for food items are created by using a machine learning model. The estimation models have prediction functions specifying timelines during which the food items are not spoiled and are in a condition for consumption. Prediction function solutions are generated for a food item inventory to indicate menu items for respective time periods. Each menu item includes food item(s) which are in condition for consumption based on the estimation models. Menu recommendations corresponding to the time periods are generated. Each menu includes menu item(s) indicated by the prediction function solutions. The menu recommendations reduce a waste of the food items through spoilage by increasing a likelihood of consumption of the food items within the respective timelines.