Forecasting day-ahead electricity load using a multiple equation time series approach

NCER Working Paper Series Forecasting day-ahead electricity load using a multiple equation time series approach

The quality of short-term electricity load forecasting is crucial to the operation and trading activities of market participants in an electricity market. In this paper, it is shown that a multiple equation time-series model, which is estimated by repeated application of ordinary least squares, has the potential to match or even outperform more complex nonlinear and nonparametric forecasting mo...

Forecasting day-ahead electricity load using a multiple equation time series approach

The quality of short-term electricity load forecasting is crucial to the operations and trading activities of market participants in an electricity market. In this paper, a multiple equation time series model for intra-day and day-ahead load forecasting is built. The model uses lagged load and temperature as the primary explanatory variables but makes effective use of diurnal characteristics an...

Further Advances in Forecasting Day-Ahead Electricity Prices Using Time Series Models

Forecasting prices in electricity markets is critical for consumers and producers in planning their operations and managing their price risk. We utilize the generalized autoregressive conditionally heteroskedastic (GARCH) method to forecast the electricity prices in two regions of New York: New York City and Central New York State. We contrast the one-day forecasts of the GARCH against techniqu...

Forecasting day ahead electricity price using ARMA methods

One-Day-Ahead Load Forecasting using nonlinear Kalman filtering algorithms

In this paper, we consider the problem of 24-hour ahead short-term load forecasting; the formulation is based on the nonlinear Kalman filtering. Our formulation takes into account weather conditions as well as previous trends. Effects of weather as well as prior consumptions are nonlinear functions; hence our choice. We compare our proposed method with the standard Kalman filtering approach and...