Electric Power Demand Forecasting Using Neural Networks
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Solution Overview
Problem
Current electric power demand forecasting methods, such as those using weather data, face challenges in achieving high accuracy due to the irregular nature of power consumption in general homes, necessitating the consideration of additional factors beyond weather data for more accurate predictions.
Innovation Solution
An electric power management apparatus and method that includes an electric power measurement block to measure consumption, a comparison block to compare measured consumption with forecasted demand, and a demand forecast block using neural networks or learning algorithms, incorporating environment and past consumption data to enhance forecasting accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If weather data and total electric power demand of the past are used for forecasting, then electric power demand can be forecast with some accuracy, but the forecast accuracy is insufficient due to the irregular nature of power consumption in general homes
Solution Approach 1:
The patent segments the forecasting task by introducing multiple forecasting apparatuses that each handle different aspects of power demand prediction. Individual forecasting apparatuses can be specialized for different types of consumers or time periods, allowing the system to adapt to irregular consumption patterns while maintaining overall forecast accuracy
Solution Approach 2:
The patent changes the parameters used in forecasting by incorporating not only weather data and historical demand but also other relevant factors such as consumer behavior patterns, appliance usage data, and real-time consumption data. This multi-parameter approach enables the system to accurately forecast demand even for irregular consumption patterns in general homes
2Measurement precision
If only weather data is used for forecasting, then the forecasting system is simple, but the forecast accuracy is insufficient because other factors affecting power demand are not considered
Solution Approach 1:
The forecasting apparatus is designed with multi-functionality to handle various types of input data (weather data, historical demand, consumer behavior, appliance data) and adapt to different forecasting scenarios. This universal design allows the system to incorporate multiple factors without requiring separate specialized systems for each data type, thus improving accuracy while controlling complexity
Solution Approach 2:
The patent introduces an intermediary learning mechanism (such as neural networks or statistical models) that processes and integrates multiple input factors including weather data, historical patterns, and other relevant variables. This intermediary layer synthesizes complex multi-factor inputs into accurate demand forecasts, allowing the system to consider numerous factors without proportionally increasing system complexity
3Adaptability or versatility
If electric power demand forecasting is performed for general homes with irregular consumption patterns, then comprehensive coverage is achieved, but accurate forecasting becomes difficult due to the irregular nature of demand
Solution Approach 1:
The forecasting system employs dynamic models that can adapt to changing consumption patterns in real-time. By using learning algorithms that continuously update based on new data, the system maintains high forecast accuracy even for general homes with irregular consumption patterns, while providing comprehensive coverage across different consumer types
Solution Approach 2:
The patent incorporates feedback mechanisms where actual consumption data is continuously compared with forecasted values, and the discrepancies are used to refine and update the forecasting models. This feedback loop enables the system to learn from irregular consumption patterns and improve accuracy over time, maintaining both comprehensive coverage and high precision for diverse consumer types
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables accurate electric power demand forecasting by considering multiple factors, improving prediction accuracy and facilitating profitable electric power trading by aligning consumer forecasts with actual consumption patterns.
Implementation Method 1
carrying out learning using a neural network
Data Source
AI summary
Disclosed herein is an electric power management apparatus including: an electric power measurement block configured to measure an electric power consumption amount of an electric power consumer; and an electric power comparison block configured to make a comparison between an electric power consumption amount measured by the electric power measurement block and an electric power demand forecast amount indicative of a forecast amount of an electric power demand of the electric power consumer.