Photovoltaic (PV) systems often generate surplus electricity during daytime when production exceeds demand. To address this, existing studies optimize energy storage and heat-pump (HP) water heater operations but typically focus only on same-day forecasts. This study proposes using regional weekly weather forecasts to enhance PV surplus management. Solar irradiance is estimated via machine learning trained on historical data, using daily and weekly forecasts as inputs. Predicted irradiance informs PV generation forecasts, guiding optimal operational planning for battery storage and HP water heaters through linear programming. Plans are adjusted based on actual generation data. Results indicate that perfectly accurate weekly forecasts could reduce surplus electricity by 16% compared to same-day forecasts. Even with estimated irradiance, integrating next-day forecasts reduces surplus by 0.66% relative to same-day predictions alone.

Creative activities, such as painting and music, are one source of satisfaction and fulfillment for people with disabilities. However, some individuals with a disability can not satisfactorily enjoy such activities because of involuntary movement or spasms. In this study, we developed a drawing assist system for patients with cerebral palsy of the tension athetosis type, who experience spasticity that makes it difficult for the assist system to distinguish involuntary movement from voluntary movement. We designed a variable filter to attenuate involuntary movement on the basis of the behavioral characteristics of the velocity component with respect to in voluntary movement. Our system enabled drawing based on the participant's own senses and motor control, even when experiencing involuntary movement.