Simulation Study on Clustering Approaches for Short‐Term Electricity Forecasting

Abstract

Advanced metering infrastructures such as smart metering have begun to attract increasing attention; a considerable body of research is currently focusing on load profiling and forecasting at different scales on the grid. Electricity time series clustering is an effective tool for identifying useful information in various practical applications, including the forecasting of electricity usage, which is important for providing more data to smart meters. This paper presents a comprehensive study of clustering methods for residential electricity demand profiles and further applications focused on the creation of more accurate electricity forecasts for residential customers. The contributions of this paper are threefold: (1) using data from 46 homes in Austin, Texas, the similarity measures from different time series are analyzed; (2) the optimal number of clusters for representing residential electricity use profiles is determined; and (3) an extensive load forecasting study using different segmentation-enhanced forecasting algorithms is undertaken. Finally, from the operator’s perspective, the implications of the results are discussed in terms of the use of clustering methods for grouping electrical load patterns.

Kernel Ridge Regression Model Based on Beta-Noise and Its Application in Short-Term Wind Speed Forecasting

“…the output value is x i+11+step , and step = 1, 3. Namely, the pattern above is used to forecast the wind speed each interval of 10 and 30 min at each Point x i+11 , respectively [30,31].…”

Revealing Household Characteristics from Electricity Meter Data with Grade Analysis and Machine Learning Algorithms

“…The purpose of neural networks is to convert input data into output data with a specific characteristic or to modify such systems of equations to read useful information from their structure and parameters. On a statistical basis, selected types of neural networks can be interpreted in general non-linear regression categories [28].…”

“…. , x p T represents the input data, W 1 is the matrix of the first layer weights with dimensions M × P, W 2 is the matrix of the second layer weights with dimensions K × M, h i (u) and b i are nonlinearities (functions of neuron activation e.g., logistic function) and constant values in subsequent layers respectively [28]. The goal of supervised learning of the neural network is to search for such network parameters that minimize the error between the desired values L i and received at the output of the network P i .…”

“…where n is the number of the training sample, P i are predicted and reference value and K is the number of training epochs of the neural network [28].…”

“…where g and H denote the gradient and the hesian of the last known solution w k , respectively [28].…”