Electricity price forecasting using a clustering approach (original) (raw)

Discovering Patterns in Electricity Price Using Clustering Techniques

Clustering is a process of grouping similar elements gathered or occurred closely together. This paper presents two clustering techniques, K-means and Fuzzy C-means, for the analysis of the electricity prices time series. Both algorithms are focused on extracting useful information from the data with the aim of model the time series behaviour and find patterns to improve the price forecasting. The main objective, thus, is to find a representation that preserves the original information and describes the shape of the time series data as accurately as possible. This research demonstrates that the application of clustering techniques is effective in order to distinguish several kinds of days. To be precise, two major groups can be distinguished thanks to the clustering: the first one that includes the working days and the second one that includes weekends and festivities. Equally remarkable is the similarity shown among days belonging to a same season.

Simulation Study on Clustering Approaches for Short Term Electricity Forecasting

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.

The Investigation of Monthly/Seasonal Data Clustering Impact on Short-Term Electricity Price Forecasting Accuracy: Ontario Province Case Study

Sustainability, 2022

The transformation of the electricity market structure from a monopoly model to a competitive market has caused electricity to be exchanged like a commercial commodity in the electricity market. The electricity price participants should forecast the price in different horizons to make an optimal offer as a buyer or a seller. Therefore, accurate electricity price prediction is very important for market participants. This paper investigates the monthly/seasonal data clustering impact on price forecasting. To this end, after clustering the data, the effective parameters in the electricity price forecasting problem are selected using a grey correlation analysis method and the parameters with a low degree of correlation are removed. At the end, the long short-term memory neural network has been implemented to predict the electricity price for the next day. The proposed method is implemented on Ontario—Canada data and the prediction results are compared in three modes, including non-clust...