Power-saving algorithms in electricity usage - comparison between the power saving algorithms and machine learning techniques

Abstract

In this paper, we propose a collaborative comparison between the online/offline algorithms for energy demand power saving purposes. Based on the Gaia Power model, resource-buffering algorithms are considered a practical peak-shaving model to effectively minimize the excessive power request. Although the algorithmic infrastructure is focused on a battery, this energy demand power saving problem is analogous to traditional demand and supply problem. In light of the similarity, we implement various machine-learning techniques, including Multiple-Layer Perceptron(MLP), Radial Basis Functions(RBF), Recurrent Neural Networks(RNN) to the identical peak-shaving model problem. In addition, the traditional naïve forecasting model and linear regression will also be discussed. Our findings suggest that the neural networks not only show faster demand smoothing in power saving algorithms, but being a nature of online algorithms is also theoretically and statistically more efficient than resource buffering algorithm and DCEC technology.

Reducing the system standby power of a personal computer

“…If a system is in the idle state or does not require high performance, dynamic scaling can lower both the operation voltage and the clock rate and thus decrease the leakage current to save dynamic power consumption [5][6][7]. On the other hand, reduction of a system's static power consumption not only increases the efficiency of the power supply module but also cuts off the power supply if the system is in the idle state [8,9].…”

“…The methods currently used to reduce these systems’ dynamic power consumption are using either hardware both to reduce the clock‐swing of the processor and to decrease the leakage current or software to monitor and predict the power consumption [3, 4]. If a system is in the idle state or does not require high performance, dynamic scaling can lower both the operation voltage and the clock rate and thus decrease the leakage current to save dynamic power consumption [5–7]. On the other hand, reduction of a system's static power consumption not only increases the efficiency of the power supply module but also cuts off the power supply if the system is in the idle state [8, 9].…”