# Renewable energy management in smart home environment via forecast embedded scheduling based on Recurrent Trend Predictive Neural Network

arXiv: https://arxiv.org/abs/2307.01622 · HTML: https://mlsanity.com/paper/2307.01622/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 55 references that are in the map.
- First posted on arXiv 4 Jul 2023.

## Builds on (most cited first, 4 of 4)

- [Deep Reinforcement Learning for Smart Home Energy Management](https://mlsanity.com/paper/1909.10165/) (2019)
- [Open Power System Data - Frictionless data for electricity system modelling](https://mlsanity.com/paper/1812.10405/) (2018)
- [Lifelong Control of Off-grid Microgrid with Model Based Reinforcement Learning](https://mlsanity.com/paper/2005.08006/) (2020)
- [Intelligent Residential Energy Management System using Deep Reinforcement Learning](https://mlsanity.com/paper/2005.14259/) (2020)

## Built on by (most cited first, 1 of 1)

- [Optimizing Appliance Scheduling for Solar Energy Management Using Metaheuristic Algorithms](https://mlsanity.com/paper/2606.13407/) (2026)

## Abstract (arXiv)

Smart home energy management systems help the distribution grid operate more efficiently and reliably, and enable effective penetration of distributed renewable energy sources. These systems rely on robust forecasting, optimization, and control/scheduling algorithms that can handle the uncertain nature of demand and renewable generation. This paper proposes an advanced ML algorithm, called Recurrent Trend Predictive Neural Network based Forecast Embedded Scheduling (rTPNN-FES), to provide efficient residential demand control. rTPNN-FES is a novel neural network architecture that simultaneously forecasts renewable energy generation and schedules household appliances. By its embedded structure, rTPNN-FES eliminates the utilization of separate algorithms for forecasting and scheduling and generates a schedule that is robust against forecasting errors. This paper also evaluates the performance of the proposed algorithm for an IoT-enabled smart home. The evaluation results reveal that rTPNN-FES provides near-optimal scheduling 37.5 times faster than the optimization while outperforming state-of-the-art forecasting techniques.