Data analysis-based time series forecast for managing household electricity consumption

Abstract

Recently, electricity consumption forecasting has attracted much research due to its importance in our daily life as well as in economic activities. This process is seen as one of the ways to manage future electricity needs, including anticipating the supply-demand balance, especially at peak times, and helping the customer make real-time decisions about their consumption. Therefore, based on statistical techniques (ST) and/or artificial intelligence (AI), many forecasting models have been developed in the literature, but unfortunately, in addition to poor choice of the appropriate model, time series datasets were used directly without being seriously analyzed. In this article, we have proposed an efficient electricity consumption prediction model that takes into account the shortcomings mentioned earlier. Therefore, the database was analyzed to address all anomalies such as non-numeric values, aberrant, and missing values. In addition, by analyzing the correlation between the data, the possible periods for forecasting electricity consumption were determined. The experimental results carried out on the Individual Household Electricity Power Consumption dataset showed a clear superiority of the proposed model over most of the ST and/or AI-based models proposed in the literature.

A granularity time series forecasting model combining three-way decision and trend information granularity

“…The contributions of this study are summarized as follows： (1) We propose an improved 𝐿 1 -trend filtering algorithm, which adds an adaptive threshold to the original algorithm. This modification allows for optimal segmentation of the original time series while preserving trend characteristics.…”

“…The future directions of this study can be summarized as follows. (1) The current research has focused on a single-layer network. Therefore, it would be valuable to explore the extension of the LSTM network architecture to more intricate scenarios.…”

“…Time series forecasting [1,2] is a crucial method for analyzing data and predicting future values or trends. It is widely used in various fields including finance, meteorology, and transportation.…”

Optimizing Building Short-Term Load Forecasting: A Comparative Analysis of Machine Learning Models

“…The primary goal of analyzing these time series data is to create a mathematical model to learn from available data to predict future observations. This has made time series forecasting an extensive research field in science and engineering [29].…”