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

TL;DR

This paper introduces rTPNN-FES, a neural network that simultaneously forecasts renewable energy and schedules appliances in smart homes, improving efficiency and robustness against errors.

Contribution

The paper presents a novel neural network architecture that integrates forecasting and scheduling for smart home energy management, reducing computational time and enhancing accuracy.

Findings

rTPNN-FES achieves near-optimal scheduling 37.5 times faster than traditional optimization.

It outperforms state-of-the-art forecasting techniques in accuracy.

The integrated approach enhances robustness against forecasting errors.

Abstract

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.