Time Series Forecasting Mastery: Intelligent Energy Consumption Analysis for Microcontrollers

Abstract

Day after day our lives dependent on electronic devices more and more such as smartphones and computers, and this is accompanied by a growing concern for managing the resources needed to power these devices. In order to use energy efficiently, it is important to be able to forecast the energy consumption of a building so that energy production can be optimized for different climatic conditions. This is particularly important in the context of smart cities and networks, which are currently an enthusiastic area of research. Recent studies [4] have shown that artificial intelligence (AI) algorithms based on long and shortterm memory (LSTM) neural networks (NNs) are very accurate at predicting energy consumption. These AI algorithms rely on collecting a long-term history of energy consumption data and associated weather data. However, processing and analyzing such a large amount of data requires significant compute and network resources, resulting in additional power consumption of cloudbased computers. To solve this problem, low-cost embedded systems can play an important role in predicting energy consumption in different climates. These small systems typically use a microcontroller and present an attractive trade-off in terms of computing power, power consumption, programming flexibility, size, and cost. However, because microcontrollers have limited processing power and memory, it is not possible to use the traditional BackPropagation (BP) algorithm to train NNs on them. Instead, the AI model is first trained and tested on a computer using a GPU for high computing power. Then, the model parameters are compressed and optimized to reduce computational complexity so that the model can be deployed on the small embedded system. This is done by reducing the number of model parameters and using efficient bit quantization without degrading the accuracy too much. In addition, interesting work has been done to run the BP algorithm on the embedded system itself. Another promising approach is transfer learning (TL), which involves training and deploying an NN on a small embedded system using pre-trained models from larger computers. TL is a wellsuited technique for deploying NNs on small embedded systems completely autonomously. So, AI algorithms based on LSTM neural networks can greatly contribute to the efficient management of energy resources. Using low-cost embedded systems, these algorithms can be deployed in various climatic contexts, without consuming excessive energy. This presents a promising solution to the challenge of energy resource management, especially in the context of smart cities and smart grids.