Home Energy Scheduling Using Forecasted Renewable Generation
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Solution Overview
Problem
Current systems for managing energy consumption in homes with renewable sources struggle to predict long-term energy availability, leading to inefficient use of renewable energy and increased reliance on the grid, resulting in higher electricity bills.
Innovation Solution
A method that uses historical consumption and production data, combined with weather forecasts, to generate a long-term schedule for activating and deactivating household appliances, optimizing energy use from renewable sources by identifying times when renewable energy is available and recommending usage accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If real-time monitoring and control logic is used to manage energy consumption, then immediate energy optimization is achieved, but long-term prediction and planning capability is lost
Solution Approach 1:
The system performs preliminary actions by generating consumption schedules in advance based on forecasted renewable energy production. The control unit creates optimized schedules that predict future energy availability and pre-plan appliance operations, allowing the system to act proactively rather than reactively to energy conditions.
Solution Approach 2:
The system dynamically adapts between real-time monitoring and long-term forecasting modes. It combines immediate sensor data with predictive algorithms that continuously update consumption schedules based on changing weather forecasts and actual energy production, creating a dynamic balance between short-term response and long-term planning.
2Productivity
If consumption scheduling is optimized for renewable energy use, then self-consumption of renewable energy is maximized, but system complexity increases
Solution Approach 1:
The control unit serves multiple functions: it monitors real-time energy production and consumption, forecasts future renewable energy availability, generates optimized consumption schedules, and controls household appliances. By consolidating these diverse functions into a single multi-functional control system, the patent reduces overall system complexity while maximizing renewable energy self-consumption.
Solution Approach 2:
The system performs self-optimization by automatically analyzing energy data, predicting future production, and adjusting appliance schedules without requiring manual intervention. The control unit independently manages the complexity of coordination between multiple appliances and energy sources, reducing the burden on users while maximizing renewable energy utilization.
3Measurement precision
If long-term forecasting is implemented to predict renewable energy production, then consumption planning accuracy is improved, but computational requirements increase
Solution Approach 1:
The system applies partial forecasting by focusing computational resources on predicting only the critical parameters needed for consumption optimization, such as renewable energy production based on weather forecasts. Rather than comprehensively modeling all possible variables, the system uses targeted predictions that achieve sufficient accuracy for scheduling decisions while limiting computational requirements.
Data Source
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AI summary
The present invention concerns a method for optimizing the electrical energy consumption coming from renewable sources (FV) in a dwelling in general, the method comprising the following steps: - Determination of an electrical energy consumption in said dwelling in a predetermined time interval (t0) and based on historical data in said time interval; - Determination of a production of energies from renewable sources present and in use in said dwelling in said predetermined time interval (t0) and based on historical data in said time interval; - Entry, in an electronic control unit, of one or more utilities (200, 300) present in the dwelling and of which to generate an activation and/or deactivation schedule in a period of time (T) subsequent to that of the determination of said historical data; - Generation of said activation and/or deactivation schedule of said utilities in said predetermined time interval (T) subsequent to that (t0) of detection of the historical data, said activation and/or deactivation schedule being obtained as a function of: - Said historical data of energy consumption and production; - One or more forecast weather data relating to said time interval (T) in which to generate the activation and/or deactivation schedule; - In such a way that said activation and/or deactivation schedule indicates, within said time interval (T), a series of times and/or time sub-intervals distributed in said time interval (T) in which to be able to activate and/or deactivate one or more of said entered utilities.