Electricity Market Clearing Price Forecasting Research Articles

Midterm Electricity Market Clearing Price Forecasting Using Two-Stage Multiple Support Vector Machine

Currently, there are many techniques available for short-term forecasting of the electricity market clearing price (MCP), but very little work has been done in the area of midterm forecasting of the electricity MCP. The midterm forecasting of the electricity MCP is essential for maintenance scheduling, planning, bilateral contracting, resources reallocation, and budgeting. A two-stage multiple support vector machine (SVM) based midterm forecasting model of the electricity MCP is proposed in this paper. The first stage is utilized to separate the input data into corresponding price zones by using a single SVM. Then, the second stage is applied utilizing four parallel designed SVMs to forecast the electricity price in four different price zones. Compared to the forecasting model using a single SVM, the proposed model showed improved forecasting accuracy in both peak prices and overall system. PJM interconnection data are used to test the proposed model.

Mid‐term electricity market clearing price forecasting using multiple least squares support vector machines

Mid-term electricity market clearing price (MCP) forecasting has become essential for resources reallocation, maintenance scheduling, bilateral contracting, budgeting and planning purposes. Currently, there are many techniques available for short-term electricity MCP forecasting, but very little has been done in the area of mid-term electricity MCP forecasting. A multiple least squares support vector machine (LSSVM) based mid-term electricity MCP forecasting model is proposed in this study. Data classification and price forecasting modules are designed to first pre-process the input data into corresponding price zones, and then forecast the electricity price. The proposed model showed improved forecasting accuracy on both peak prices and overall system compared to the forecasting model using a single LSSVM. PJM interconnection data are used to test the proposed model.

Mid-term electricity market clearing price forecasting utilizing hybrid support vector machine and auto-regressive moving average with external input

Currently, there are many techniques available for short-term electricity market clearing price (MCP) forecasting, but very little has been done in the area of mid-term electricity MCP forecasting. Mid-term electricity MCP forecasting has become essential for resources reallocation, maintenance scheduling, bilateral contracting, budgeting and planning purposes. A hybrid mid-term electricity MCP forecasting model combining both support vector machine (SVM) and auto-regressive moving average with external input (ARMAX) modules is presented in this paper. The proposed hybrid model showed improved forecasting accuracy compared to forecasting models using a single SVM, a single least squares support vector machine (LSSVM) and hybrid LSSVM-ARMAX. PJM interconnection data have been utilized to illustrate the proposed model with numerical examples.

Mid-term electricity market clearing price forecasting: A multiple SVM approach

In a deregulated electric market, offering the appropriate amount of electricity at the right time with the right bidding price is of paramount importance for utility companies maximizing their profits. Mid-term electricity market clearing price (MCP) forecasting has become essential for resources reallocation, maintenance scheduling, bilateral contracting, budgeting and planning. Although there are many techniques available for short-term electricity MCP forecasting, very little has been done in the area of mid-term electricity MCP forecasting. A multiple support vector machine (SVM) based mid-term electricity MCP forecasting model is proposed in this paper. Data classification and price forecasting modules are designed to first pre-process the input data into corresponding price zones, and then forecast the electricity price. The proposed model showed improved forecasting accuracy on both peak prices and overall system compared with the forecasting model using a single SVM. PJM interconnection data are used to test the proposed model.

Mid-term electricity market clearing price forecasting: A hybrid LSSVM and ARMAX approach

A hybrid mid-term electricity market clearing price (MCP) forecasting model combining both least squares support vector machine (LSSVM) and auto-regressive moving average with external input (ARMAX) modules is presented in this paper. Mid-term electricity MCP forecasting has become essential for resources reallocation, maintenance scheduling, bilateral contracting, budgeting and planning purposes. Currently, there are many techniques available for short-term electricity market clearing price (MCP) forecasting, but very little has been done in the area of mid-term electricity MCP forecasting. PJM interconnection data have been utilized to illustrate the proposed model with numerical examples. The proposed hybrid model showed improved forecasting accuracy compared to a forecasting model using a single LSSVM.