Short Term Demand Forecasting Using Stochastic Hour Ahead Proportion Analysis John C. Placente and Rafael G. Maramba Abstract Short term demand forecasting is very important in power systems planning, operations, risk and contingency analysis. Traditional models such as simple moving average, weighted moving average, exponential smoothing and linear regression have been usually used to carry out short term load prediction. Past studies involved time, temperature, humidity and other weather conditions as factors in determining future load. The thesis proposes a novel mathematical demand forecast model and a new prediction factor based on Stochastic Hour Ahead Proportion (SHAP). Using the 2008 Philippine Electricity Market Corporation (PEMC) historical demand as forecast simulation data, SHAP forecast model undergo several evaluations such as accuracy characterization thru calculation of Mean Absolute Percentage Error (MAPE) and Standard Deviation of Error (SDE). SHAP’s accuracy was also compared with other traditional models using statistical T-test. On a 24 hour ahead forecasting interval, SHAP offers statistically better MAPE and SDE of 2.457% and 1.634%, respectively, as compared with the traditional models’ MAPE and SDE of 4.556% and 1.976%. Satisfactory results have been obtained to prove that SHAP is adequate to provide accurate demand forecasts. The study finally suggests the acceptance of SHAP analysis as a new power system short term demand forecast method.
Deviation of Error (SDE). The study is also to compare the results obtained from the new method with simple moving average, weighted moving average, exponential smoothing and linear regression methods. II. PROPOSED FORECAST MODEL Hour Ahead Proportion (HAP) is the ratio between the electric demand value at time T+1 hour and at time T. For example, in a certain hour 1AM, the demand is 5000 MW. At the next hour, which is 2AM, the demand is 4500 MW. Therefore, the HAP of 1AM and 2AM will be 4500 MW / 5000 MW which is equal to 0.90. Taking a wider period of one day, 24 values of HAP can be generated. For example, Table I shows an example of HAP computation in 24 hours. Taking a wider period of 30 days, there will be 30 HAP values each for 1AM, 2AM up to 24AM. There will be a lot of HAP values that can be clustered hourly and daily. Collecting all of the HAP values and consider these as past observations, it can be later used for statistical treatment. Forecasting for the next period HAP at time T hour, with the use quantitative analysis and statistical measurements, the Stochastic Hour Ahead Proportion (SHAP) can be obtained.
I. INTRODUCTION
A. SHAP Determination Algorithm
SHORT term demand forecasting is very important in power systems operations and planning. Time-ahead demand information is needed in power plant generation scheduling, equipment maintenance, purchases of fuel, load dispatch, coordination, security and contingency analyses.
In order to determine the value of the SHAP for a certain hour of a specific day, follow the following algorithm:
One of the factors that needs to be further studied in load forecasting is the Stochastic Hour Ahead Proportion (SHAP) analysis. Hour Ahead Proportion (HAP) can be quantified by the ratio of demand at time T + 1 hour and the demand at time T. Using the historical data, statistical measurements, risk analysis and decision making process, the Hour Ahead Proportion can be stochastically obtained, thereby creating SHAP. By multiplying SHAP with the previous hour demand value, the next hour demand can be forecasted. SHAP analysis is a newly developed mathematical forecast model and thesis made by the author. The purpose of this study is to fully evaluate the accuracy of SHAP forecast model using the Philippine Electricity Market Corporation (PEMC) historical actual demand as experimental data. The study aims to test the model on hour-ahead, two-hour ahead, up to 24-hour ahead forecasting durations. The test indicators for the model efficiency shall be the Absolute Percentage Error (APE), Mean Absolute Percentage Error (MAPE) and Standard
1. Obtain N HAP values coming from the previoussame days of previous N weeks. The HAP value to obtain should be of the same day as the day being forecasted. The number of weeks, N, shall be the analysts’ decision based on simulating the obtained errors from N=2, 3, 4, 5 and so on. Objective is to use the N where it yields the most accurate forecast among simulation samples. For the thesis, N=6 shall be used. 2. Name the N HAPs as HAPW1, HAPW2, HAPW3, HAPW4 … HAPWN. HAPWN means the HAP coming from a certain similar day, N weeks away from the focus of forecast. 3. Determine if the HAP obtained is coming from a special day such as non-working holiday, boxing event, major power failure day, and etc. 4. If the HAP values obtained from the last N weeks are special events, eliminate this HAP and secure another HAP coming N+1 week (if the N+1 week is a special event, eliminate the N+1 week HAP and then secure the N+2 week HAP). 5. Calculate the mean of the obtained N HAP values. The mean of the HAP values taken from the six weeks will be called as the SHAP value. 6. The SHAP value will then be used for next forecast.
THE ELECTRICAL ENGINEER MAGAZINE 4TH QUARTER 2010 35