Data for : An Enhanced CNN-LSTM Based Multi-Stage Framework for PV and Load Short-Term Forecasting: DSO Scenarios

Published: 24 July 2023| Version 1 | DOI: 10.17632/zympd537wv.1
Mohammad Al-Jaafreh,


The provided data is linked to the paper titled "An Enhanced CNN-LSTM Based Multi-Stage Framework for PV and Load Short-Term Forecasting: DSO Scenarios." In this research, a novel multi-stage framework for PV and load Short-Term Forecasting (STF) is introduced, incorporating feature generation, feature selection, and optimal hyperparameter tuning preprocessing techniques. The final stage of the proposed framework presents an enhanced hybrid CNN-LSTM deep learning model architecture. The effectiveness of this framework is evaluated and compared against other state-of-the-art approaches across various DSO scenarios, encompassing multiple single-phase residential loads, three-phase feeders, and secondary substations. Remarkably, the proposed framework exhibits significant reductions in forecasting errors. The provided time series data serves the purpose of testing the proposed short-term forecasting methodology. It features a 5-minute resolution for one month (July 2021, a summer month) and consists of 8,920 data points for each data profile. This data can be categorized into two main categories: load data and PV data. The load data includes three sub-categories representing different DSO scenarios: Multiple residential loads (with 100 and 400 residential loads in separate datasets), three-phase feeder load demands, and three-phase substation load demands. On the other hand, the PV data folder contains PV data for one month (July), along with corresponding weather data.


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This work was supported by Innovate UK GCRF Energy Catalyst Pi-CREST project under Grant number 41358


University of Bradford


Forecasting, Energy Demand, Electric Power Distribution, Residential Energy Conservation


Innovate UK