Induced Markov Chain Wind Power Forecasting
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Solution Overview
Problem
Current wind farm power generation forecasting methods are plagued by limited accuracy, leading to economic and environmental inefficiencies due to the uncertainty in wind power contributions to the grid, resulting in over- or under-production of electricity from baseline sources.
Innovation Solution
The implementation of an induced Markov chain (IMC) model for very short-term wind power forecasting, which transforms continuous power output measurements into discrete states based on the difference process, reducing state space complexity and enabling point and distributional forecasts with lower computational complexity and improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional forecasting methods are used, then computational complexity is low, but forecast accuracy is limited
Solution Approach 1:
The patent segments the continuous power output data into discrete states by dividing the power range into intervals. This segmentation transforms the continuous forecasting problem into a discrete state transition problem, enabling the use of Markov chain models that achieve improved forecast accuracy through state-to-state transition probabilities while maintaining computational efficiency.
Solution Approach 2:
The patent changes the parameter representation from continuous power values to discrete state indices. By transforming the power output parameter into discrete states and using transition matrices to represent state evolution, the model achieves better accuracy through probabilistic forecasting while keeping computational complexity manageable through matrix operations.
2Loss of information
If continuous power output data is used directly, then information is preserved, but state space complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the continuous power output range into discrete intervals, creating a finite state space. This segmentation reduces the infinite continuous state space to a manageable discrete set of states, enabling practical implementation of Markov chain models while preserving essential information through the transition probability structure.
Solution Approach 2:
The patent creates a simplified copy of the continuous system by representing power states as discrete indices. This copied discrete state space mirrors the essential dynamics of the continuous system through transition probabilities, reducing complexity while retaining the core informational structure needed for accurate forecasting.
3Device complexity
If Markov chain models are used, then computational complexity is reduced, but forecast accuracy may be limited without proper state definition
Solution Approach 1:
The patent performs preliminary action by pre-defining the state space and computing transition matrices from historical data before actual forecasting. This preliminary setup includes segmenting the power range into intervals and calculating transition probabilities in advance, which enables the model to achieve high accuracy during operation with minimal computational complexity since the heavy lifting is done beforehand.
Solution Approach 2:
The patent incorporates feedback by using historical transition data to build the transition matrix, which then guides future forecasts. The model learns from past state transitions and uses this learned information to improve forecast accuracy, creating a feedback loop where historical performance informs future predictions while maintaining computational efficiency.
Data Source
AI summary
Systems and methods for forecasting power generation in a wind farm are disclosed. The systems and methods utilize an induced Markov chain model to generate a forecast of power generation of the wind farm. The forecast is at least one of a point forecast or a distributional forecast. Additionally, the systems and methods modify at least one of: (i) a generation of electricity at a power plant coupled to a common power grid as the wind farm; or (ii) a distribution of electricity in the common power grid based on the forecast of power generation of the wind farm. In an exemplary approach, utilizing the induced Markov chain model to generate the forecast may include determining a series of time adjacent power output measurements based on historical wind power measurements and calculating a time series of difference values based on the series of time adjacent power output measurements.