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• W4313444732 startingPage "457" @default.
• W4313444732 abstract "Today, the world experiences a surge in adoption of renewable electricity generation methods. As a consequence to this, the dependence of electricity supply and demand has parallelly seen a dramatic increase on covariates like solar irradiance, wind speed, and temperature. Addressing the demand response scenario with efficient forecasting strategies is critical for the smart grid stability. With increment of renewable energy factor in total share of electricity generation, it becomes imperative to consider the external factors covariates while predicting the electricity price and load forecasting. Forecasting strategies, based on statistical and machine learning models, facilitates efficient and informed responses for electricity market. However, due to presence of large pool of forecasting models each having its own suitability, it becomes cumbersome to choose an appropriate sophisticated model selection from them. This work presents a methodological review of existing forecasting statistical and machine learning models and algorithms, with the prime purpose of choosing a best suited model for a specific environment or scenario. Data used for this purpose are a time series data, and information is very scattered in nature; thus, the process to make a full dataset and combining it with external covariates is a complex phenomenon. The work also discusses the process of making the multivariate time series dataset based on U.S. Energy Information Administration and combined it with typical meteorological year (TMY3) datasets. To validate the outcome of literature survey, detail experiment has been performed using the prepared dataset, and comparative analysis is presented." @default.
• W4313444732 created "2023-01-06" @default.
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• W4313444732 date "2023-01-01" @default.
• W4313444732 modified "2023-10-03" @default.
• W4313444732 title "A Methodological Review of Time Series Forecasting with Deep Learning Model: A Case Study on Electricity Load and Price Prediction" @default.
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• W4313444732 doi "https://doi.org/10.1007/978-981-19-5868-7_34" @default.
• W4313444732 hasPublicationYear "2023" @default.
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