Data on Smart Home Energy Flow Optimization Time Series from Practical Case Studies

This dataset provides a comprehensive representation of the energy flows, component states, and control variables of a residential smart home equipped with photovoltaic (PV) generation, a battery energy storage system (BESS), a ground source heat pump (GSHP), deferrable appliances (DAs), and an electric vehicle (EV) smart charger (SC). The data originate from a one-year simulation performed with a 15-minute time resolution, reproducing the realistic operation of a single-family dwelling connected to the distribution grid.

For a better reading of results, datetime array has been set to 2025.

The dataset is structured into four folders, each corresponding to one of the simulated case studies discussed in "Smart home energy flow optimization, a practical case study",. Energy Conversion and Management: X, https://doi.org/10.1016/j.ecmx.2025.101340:

-Baseline (woEMS): reference scenario without any Energy Management System (EMS), where no flexibility services are considered.
-Self-Consumption (sc): optimization promoting local utilization of PV generation by minimizing energy imported from the grid.
-Smoothing (sm): optimization aiming to minimize squared temporal variations in grid exchange, producing smoother profiles.
-Baseline Profile Matching (bp): optimization minimizing deviations from a flat baseline energy profile, encouraging balanced daily exchanges.

Each folder includes time-series data in JSON format, describing for every 15-minute time step:

- Active power flows between the grid and smart home subsystems.
- Input variables such as external temperature, thermal demand, load availability, and user constraints.
- State variables including state of charge (SoC) of batteries, tank temperature, and appliance cycle states.

A complete mapping between symbols, variable names, and data columns is provided in the included README file.
All files use consistent naming conventions and physical units to facilitate integration, comparison, and reuse. Diagnostic figures and quick visualizations can be generated using the provided Python and MATLAB scripts (plotSmartHomeData.py and plotSmartHomeData.m), which reproduce main power trajectories and component state trends over a selected time horizon.

This dataset can be used for benchmarking, model validation, and data-driven research in residential energy management, grid flexibility assessment, and smart home optimization. It provides a reproducible, high-resolution framework suitable for the training and testing of optimization algorithms and machine learning models for predictive EMS development.