A Random Forest Method for Real-Time Price Forecasting in New York Electricity Market

1. A Random Forest Method for Real-Time Price Forecasting in New York Electricity Market Paper No: 14PESGM2284 Jie Mei, Guannan Qu, Dawei He, Ronald G. Harley and Thomas G. Habetler Georgia Institute of Technology jmei8@gatech.edu

2. Background • Electricity price is volatile and difficult to be predict Fig.1. Real-time electricity price at Central Park area, NY, on Jun 30th, 2013. • Limitations for traditional methods • Credible? • Self-adaptable?

3. Method: Basic idea f(X)0 Step 1: Set up feature pool Step 2: Build a regression decision tree Step 3: Build random forest Step 4: Computing the final forecasting according to the mean of all the trees of random forest

4. Method: Flow Chart Input: last observation Forecast price three hours later Deliver last observation to RF If Deliver number<k Last forecasting Error>e If the receiving data size>a And forecasting error <b Split branches and update RF • Enhance self-adaptability by means of feedback module

5. Results Fig.2. Forecasting results of ANN, ARMA and RF. Adaptive random forest (RF) is more accurate than ANN and ARMA

6. Conclusions • Proposed an adaptive random forest method for electricity price forecasting • High self-adaptability • High credibility: Confidence interval • Future work • Enlarge feature pool • Method for detecting spikes