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

Abstract

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-clustering, seasonal, and monthly clustering. The studies show that the prediction error in the monthly clustering mode has decreased compared to the non-clustering and seasonal clustering modes in two different values of the correlation coefficient, 0.5 and 0.6.

The Role of Resource Consumption Accounting in Achieving Competitive Prices and Sustainable Profitability

“…Additionally, apart from RCA practices serving an essential role in capturing vital non-financial information like customer satisfaction and services quality, Gougheri and others' suggestions about the overlooked speed, electricity prices, and load demand stochastic parameters can be integrated into RCA pricing strategies resulting in competitive prices in energy markets [27]. This supports novel constellations of ideas about RCA representing a sustainable pricing tool energy markets buyers and sellers can be utilised to set competitive prices and achieve their goals [31]. Thus, hypothesis one was accepted, asserting that resource consumption accounting has a significant positive and direct influence on competitive prices.…”

“…Amid the prevalence of other problems undermining the effective functioning of electricity markets, Pourhaji and others highlighted that the commercialisation of electricity as a commercial commodity limits gains available to market participants [31]. Suggested measures denote those optimal offers by sellers or buyers are achievable when electricity price participants forecast the price in different horizons [31].…”

“…Amid the prevalence of other problems undermining the effective functioning of electricity markets, Pourhaji and others highlighted that the commercialisation of electricity as a commercial commodity limits gains available to market participants [31]. Suggested measures denote those optimal offers by sellers or buyers are achievable when electricity price participants forecast the price in different horizons [31]. Furthermore, optimal power management systems in deregulated energy markets require accurate price forecasting [32] and the application of resource consumption accounting methods stands as one of the significant novel and effective approaches required in fostering the implementation of sustainable prices in electricity markets.…”

Optimized Data-Driven Models for Short-Term Electricity Price Forecasting Based on Signal Decomposition and Clustering Techniques

“…The proposed forecast model surpasses current state-of-the-art forecasting algorithms, demonstrating a significant improvement in prediction accuracy. In a different approach to data clustering, Pourhaji et al [28] investigate the seasonal data clustering effect on price forecasting. The energy price forecasting for the day-ahead horizon is based on data from Ontario province in Canada.…”