Fog intelligence for energy efficient management in smart street lamps

Abstract

Street lamp is a great asset for human society with a narrow beam spread light. The extensive proliferation of solar power in street lamps causes power outages due to their variable power-generated profiles. Thus Smart Street Lamp Fog Intelligence (SSLFI) framework based on hierarchical learning was proposed for efficient energy management in solar street lamps. Smart Street Lamp (SSL) shifts its brightness at higher and lower light levels with a comforting, energy-efficient gleam of light. The fog intelligence framework forecasts the SSL output power through short-term probabilistic energy consumption forecasts using Q-NARX-BiLSTM (Quantile Regression-Nonlinear Auto-Regressive Neural Networks with exogenous input-Bidirectional Long short-term memory) model. NARX-BiLSTM of two module types: (1) NARXNN (Nonlinear Auto-Regressive Neural Networks with exogenous input) model generates SSL power consumption and (2) BiLSTM (Bidirectional Long short-term memory) model generates SSL power forecasts. The quantile regression with the NARX-BiLSTM (Nonlinear Auto-Regressive Neural Networks with exogenous input-Bidirectional Long short-term memory) model forecasts the seasonal patterns achieving non-parametric interval predictions. The probabilistic predictions of power consumption are determined based on the conditional quantile using an improved kernel density estimation approach. The fuzzy inference system adopts forecasting results to diagnose fault conditions in street lamps. The experiment results show that the proposed framework SSLFI outperformed the state-of-the-art models forecasting under different weather conditions.