18100933. METHOD FOR COMBINING CLASSIFICATION AND FUNCTIONAL DATA ANALYSIS FOR ENERGY CONSUMPTION FORECASTING simplified abstract (Hitachi, Ltd.)
METHOD FOR COMBINING CLASSIFICATION AND FUNCTIONAL DATA ANALYSIS FOR ENERGY CONSUMPTION FORECASTING
Organization Name
Inventor(s)
Aniruddha Rajendra Rao of San Jose CA (US)
Chandrasekar Venkatraman of Saratoga CA (US)
Chetan Gupta of San Mateo CA (US)
METHOD FOR COMBINING CLASSIFICATION AND FUNCTIONAL DATA ANALYSIS FOR ENERGY CONSUMPTION FORECASTING - A simplified explanation of the abstract
This abstract first appeared for US patent application 18100933 titled 'METHOD FOR COMBINING CLASSIFICATION AND FUNCTIONAL DATA ANALYSIS FOR ENERGY CONSUMPTION FORECASTING
The patent application describes systems and methods for forecasting short-term energy consumption based on time-series data related to energy usage in different types of buildings in various climatic zones.
- Receipt of time-series data on energy consumption for different types of buildings and climatic zones
- Execution of random convolutional kernel (RCK) on the data to classify it by building type and climatic zone
- Use of a trained functional neural network (FNN) on the classified data to forecast short-term energy consumption
Potential Applications: - Energy management systems for buildings - Climate control optimization in different regions - Forecasting energy demand for utility companies
Problems Solved: - Improving energy efficiency in buildings - Enhancing climate-specific energy consumption predictions - Providing accurate short-term energy forecasts
Benefits: - Cost savings through optimized energy usage - Reduction of environmental impact - Improved planning for energy demand
Commercial Applications: Title: Energy Forecasting System for Buildings This technology can be used by building management companies, utility providers, and environmental agencies to optimize energy usage, reduce costs, and improve sustainability efforts in various climatic regions.
Prior Art: Readers can explore prior research on energy forecasting models, neural networks, and convolutional kernels in the field of energy management and climate control.
Frequently Updated Research: Stay informed about the latest advancements in energy forecasting models, neural network algorithms, and climate-specific energy consumption trends to enhance the accuracy and efficiency of energy predictions.
Questions about the Technology: 1. How does this technology improve energy efficiency in buildings? 2. What are the key factors influencing short-term energy consumption forecasts?
Original Abstract Submitted
Example implementations described herein involve systems and methods that can include, for receipt of time-series data indicative of energy consumption associated with a type of building of a plurality of different types of buildings and a climatic zone from a plurality of climatic zones, executing random convolutional kernel (RCK) on the time-series data to generate a classification group of the time-series data according to type of building and the climatic zone; and executing a trained functional neural network (FNN) on the time-series data of the classification group to provide a short-term energy consumption forecast.
