Building Energy Time Series Data Mining for Behavior Analytics and Forecasting Energy consumption

Abstract

The significant aim of this research has always been to evaluate the mechanism for efficient and inherently aware usage of vitality in-home devices, thus improving the information of smart metering systems with regard to the usage of selected homes and the time of use. Advances in information processing are commonly used to quantify gigantic building activity data steps to boost the activity efficiency of the building energy systems. Here, some smart data mining models are offered to measure, and predict the time series for energy in order to expose different ephemeral principles for using energy. Such considerations illustrate the use of machines in relation to time, such as day hour, time of day, week, month and year relationships within a family unit, which are key components in gathering and separating the effect of consumers behaviors in the use of energy and their pattern of energy prediction. It is necessary to determine the multiple relations through the usage of different appliances from simultaneous information flows. In comparison, specific relations among interval-based instances where multiple appliances use continue for certain duration are difficult to determine. In order to resolve these difficulties, an unsupervised energy time-series data clustering and a frequent pattern mining study as well as a deep learning technique for estimating energy use were presented. A broad test using true data sets that are rich in smart meter data were conducted. The exact results of the appliance designs that were recognized by the proposed model were filled out by Deep Convolutional Neural Networks (CNN) and Recurrent Neural Networks (LSTM and GRU) at each stage, with consolidated accuracy of 94.79%, 97.99%, 99.61%, for 25%, 50%, and 75%, respectively.

Prediction and Optimization Analysis of the Performance of an Office Building in an Extremely Hot and Cold Region

“…The CNN deep neural network model is used to predict the building performance, and it can abolish problems such as the low prediction accuracy of traditional data-driven models. Meanwhile, CNNs with backpropagation algorithms can automatically adjust the network parameters to minimize the loss function, thus improving the performance of the network, i.e., it can enhance the predictive performance of buildings and minimize the time cost [64][65][66][67][68][69][70][71][72][73][74][75][76][77]. For example, the following scholars have conducted relevant studies at this level: Yue et al investigated the application of data-driven modeling to building energy consumption and indoor environments by studying.…”