Abstract: Embodiments provide systems and methods for modeling future asset value, which may establish planning simulation and optimization parameters for an individual, execute a planning simulation based on the parameters, analyze results of the planning simulation to determine candidate plans with highest chances of success, determine, from among the candidate plans, an optimal plan, based on executing concurrent gradient descent that includes executing steps of gradient descent in parallel, and report the optimal plan. Embodiments may also provide systems and methods for describing a portfolio and investment goals in a manner comprehensible by the investor, modeling financial plans that meet those goals, simulating those plans over time, selecting the plans that demonstrate the greatest utility for the investor, executing those plans in the market, and continually revising plans as circumstances change.

Abstract: Embodiments provide systems and methods for modeling future asset value, which may establish planning simulation and optimization parameters for an individual, execute a planning simulation based on the parameters, analyze results of the planning simulation to determine candidate plans with highest chances of success, determine, from among the candidate plans, an optimal plan, based on executing concurrent gradient descent that includes executing steps of gradient descent in parallel, and report the optimal plan. Embodiments may also provide systems and methods for describing a portfolio and investment goals in a manner comprehensible by the investor, modeling financial plans that meet those goals, simulating those plans over time, selecting the plans that demonstrate the greatest utility for the investor, executing those plans in the market, and continually revising plans as circumstances change.

Abstract: Certain embodiments include receiving load data from an energy generation (EG) system, where the load data is provided by a load meter, and where the load data corresponds to a predetermined time period. The predetermined time period may be during peak daylight hours (e.g., between 10 A.M. and 2 P.M). The method includes receiving EG data from the EG system, where the EG data corresponds to EG generation by an EG circuit over the predetermined time period, and determining if an inverse relationship exists between the load data and EG data. The method includes determining that the load meter is installed upstream from the EG circuit if an inverse relationship between the load data and EG data exists, and determining that the load mater is installed downstream from the EG circuit if an inverse relationship between the load data and EG data does not exist.

Abstract: Certain embodiments include receiving load data from an energy generation (EG) system, where the load data is provided by a load meter, and where the load data corresponds to a predetermined time period. The predetermined time period may be during peak daylight hours (e.g., between 10 A.M. and 2 P.M). The method includes receiving EG data from the EG system, where the EG data corresponds to EG generation by an EG circuit over the predetermined time period, and determining if an inverse relationship exists between the load data and EG data. The method includes determining that the load meter is installed upstream from the EG circuit if an inverse relationship between the load data and EG data exists, and determining that the load mater is installed downstream from the EG circuit if an inverse relationship between the load data and EG data does not exist.