Nonparametric regression based short-term load forecasting

Abstract

This paper presents a novel approach to short-time load forecasting by the application of nonparametric regression. The method is derived from a load model in the form of a probability density function of load and load affecting factors. A load forecast is a conditional expectation of load given the time, weather conditions and other explanatory variables. This forecast can be calculated directly from historical data as a local average of observed past loads with the size of the local neighborhood and the specific weights on the loads defined by a multivariate product kernel. The method accuracy relies on the adequiite representation of possible future conditions by historical data, but a measure to detect any unreliable forecast can be easily constructed. The proposed procedure requires few parameters that can be: easily calculated from historical data by applying the cross-validation technique.

Short-term load forecasting in air-conditioned non-residential Buildings

“…weather). In this latter group we can place ARMA models (also known as Box-Jenkins [11]), ARMAX models [12], optimization techniques [13], non-parametric regression [14], structural models [15], and diverse curve-fitting procedures [16]. In spite of the large number of alternatives, however, linear regressions [17] have been the most popular election, and, most accurately, ARIMA has been the technique showing the most promising results [18].…”

A Literature Survey of Load Forecasting Methods and Impact of Different Factors on Load Forecasting

“…Slope coefficients measure the sensitivity of the dependent variable that how they changes with the independent variable. The future value of the dependent variable can be estimated [7] [8] [9] [10]. Time series analysis: The sequence of data typically at successive uniform intervals is used in Time series Analysis.…”

Demand Management and Wireless Sensor Networks in the Smart Grid

“…Regression methods aim to model the relationship of load and environmental factors, e.g. temperature (Charytoniuk et al, 1998 , the authors use a simple AR process that uses the price values of previous two days and the same day of the last week. This is due to the weekly pattern of the consumption data.…”