Energy Prediction

Published: 30 November 2023| Version 1 | DOI: 10.17632/c4rn7mtfrf.1
Contributor:
Vijayalaxmi Beeravalli

Description

Dataset Name: PV Yield Forecasting Data Set Data Format: The dataset is provided in CSV format, consisting of time-stamped records. Features: Time Series Data: Date and time stamps for each record, ranging from 1990 Jan to 2014 Dec. PV Output Readings: Measurements of Photovoltaic output in kWh. Environmental Factors: Includes solar irradiance, temperature, humidity, and cloud cover. Processed Features: Data columns processed for XGBoost and LSTM models, including seasonal components. Time Range and Frequency: Data spans from 1990 to 2014, with hourly recording frequency. Pre-processing and Cleaning: The dataset has been pre-processed for anomalies and missing values, and features have been selected and cleaned for optimal model input. Purpose: This data is used to develop and test a novel forecasting framework combining XGBoost, time series decomposition, and LSTM for both short and long-term solar yield predictions. Performance Metrics: Includes columns for prediction accuracy, nRMSE values for the developed framework, and comparison with benchmark models. Comparative Model Data: Results from LSTM, FRNN, NNE, and other models for comparative analysis. Dataset Size and Scope: Contains approximately [X million] records, covering various geographical locations and types of PV plants. Additional Notes: This dataset is part of a comprehensive study aiming to enhance the integration of PV plant output with the main power grid, as detailed in our research paper.

Files

Institutions

Central Queensland University

Categories

Time Series Forecasting

Licence