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Electricity Forecasting

This dataset contains hourly electricity demand data and corresponding weather indicators collected from 2021 to 2023. The electricity data was sourced from the U.S. Energy Information Administration (EIA), covering both winter and summer periods across three years. Weather features—including temperature, wind speed, and humidity—were collected to capture the external conditions affecting demand. All files are stored in CSV format and aligned by timestamp. This dataset supports research in time series forecasting, demand prediction, and energy systems modeling.

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Over the past two decades,  various machine learning methods are proposed for the prediction of electricity prices.  Existing literature discusses in detail the strength and weaknesses of these methods.  This dataset focuses on the exploration of online personalized machine learning models for the prediction of monthly electricity bills in developing countries. For this purpose, we gathered the dataset of monthly electricity bills of 70 users for one year.

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