Short-Term Load Forecasting Using AMI Data

• URL: http://arxiv.org/abs/1912.12479v5
• Date: Mon, 16 May 2022 16:04:51 GMT
• Title: Short-Term Load Forecasting Using AMI Data
• Authors: Haris Mansoor, Sarwan Ali, Imdadullah Khan, Naveed Arshad, Muhammad Asad Khan, Safiullah Faizullah
• Abstract summary: This paper proposes a method called Forecasting using Matrix Factorization (textscfmf) for short-term load forecasting (textscstlf) textscfmf only utilizes historical data from consumers' smart meters to forecast future loads. We empirically evaluate textscfmf on three benchmark datasets and demonstrate that it significantly outperforms the state-of-the-art methods in terms of load forecasting.
• Score: 0.19573380763700707
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate short-term load forecasting is essential for the efficient operation of the power sector. Forecasting load at a fine granularity such as hourly loads of individual households is challenging due to higher volatility and inherent stochasticity. At the aggregate levels, such as monthly load at a grid, the uncertainties and fluctuations are averaged out; hence predicting load is more straightforward. This paper proposes a method called Forecasting using Matrix Factorization (\textsc{fmf}) for short-term load forecasting (\textsc{stlf}). \textsc{fmf} only utilizes historical data from consumers' smart meters to forecast future loads (does not use any non-calendar attributes, consumers' demographics or activity patterns information, etc.) and can be applied to any locality. A prominent feature of \textsc{fmf} is that it works at any level of user-specified granularity, both in the temporal (from a single hour to days) and spatial dimensions (a single household to groups of consumers). We empirically evaluate \textsc{fmf} on three benchmark datasets and demonstrate that it significantly outperforms the state-of-the-art methods in terms of load forecasting. The computational complexity of \textsc{fmf} is also substantially less than known methods for \textsc{stlf} such as long short-term memory neural networks, random forest, support vector machines, and regression trees.

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