Developing a Mixed Neural Network Approach to Forecast the Residential Electricity Consumption Based on Sensor Recorded Data

Abstract

In this paper, we report a study having as a main goal the obtaining of a method that can provide an accurate forecast of the residential electricity consumption, refining it up to the appliance level, using sensor recorded data, for residential smart homes complexes that use renewable energy sources as a part of their consumed electricity, overcoming the limitations of not having available historical meteorological data and the unwillingness of the contractor to acquire such data periodically in the future accurate short-term forecasts from a specialized institute due to the implied costs. In this purpose, we have developed a mixed artificial neural network (ANN) approach using both non-linear autoregressive with exogenous input (NARX) ANNs and function fitting neural networks (FITNETs). We have used a large dataset containing detailed electricity consumption data recorded by sensors, monitoring a series of individual appliances, while in the NARX case we have also used timestamps datasets as exogenous variables. After having developed and validated the forecasting method, we have compiled it in view of incorporating it into a cloud solution, being delivered to the contractor that can provide it as a service for a monthly fee to both the operators and residential consumers.

A Review of the Recent Developments in Integrating Machine Learning Models with Sensor Devices in the Smart Buildings Sector with a View to Attaining Enhanced Sensing, Energy Efficiency, and Optimal Building Management

“…A total of 45.5% of the scientific articles summarized in Table S10, presented in the Supplementary Materials file, analyzed smart buildings in general; the same percentage of papers considered smart homes, while the remaining 9% analyzed both smart homes and smart buildings. The authors of these scientific papers make use of different types of sensors in their analyses, including sensors for registering the electricity consumption [22]; Wireless Sensor Networks (WSNs) [23,45,96]; Passive Infrared (PIR) sensors or motion detectors [75,97]; smart metering systems and sensors installed by the residential consumer, corresponding to 15 individual appliances [95]; weather sensors [12]; flowmeter sensors [43]; temperature sensors, external humidity sensors, solar radiation sensors [98]; thermal sensors [2]; and door/window entry point sensors, electricity power usage sensors, bed/sofa pressure sensors, and flood sensors [75].…”

“…With respect to the reasons for implementing the Neural Networks for regression purposes integrated with sensor devices in smart buildings, these were mainly related to forecasting electricity consumption [12,22,23,45,95]; identifying the occurrence of a specific pattern in a Water Management System (WMS) [43]; indoor temperature monitoring and forecasting [96,98]; human behavior recognition [2,75]; and short-term prediction of occupancy [97].…”

“…With respect to the devised research methods, in [22], The performance metrics considered in the scientific papers using the Neural Networks for Regression Purposes integrated with sensor devices in smart buildings included Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) [12,22,23,45]; Coefficient of Determination (R 2 ) [23]; Mean Absolute Percentage Error (MAPE) [23,45]; Mean Squared Error (MSE) [45,95,98]; Correlation Coefficient (R) [95,98]; the differences between the real consumption and the forecasted ones [95]; Pearson Coefficient [12]; Accuracy [43,97]; Precision, Recall, and F-Measure [43]; Average Error and Error Rate [2]; Mean Absolute Error (MAE) [96]; and Root Mean Squared Error (RMSE) [75].…”

“…In [95], Oprea et al presented a forecasting method for providing accurate predictions of electricity consumption at the residential level, refined to the electrical devices level. The authors considered smart home complexes that were capable of partially sustaining their electricity consumption based on renewable energy resources.…”

Predicting Heat Meters’ Failures With Selected Machine Learning Models

“…The previous works on meters focus mainly on the so-called 'smart meters', which most frequently provide large amounts of information concerning the current consumption of electric energy ( [15], [16] or [17]). The analysis of this data usually regards the predicted energy consumption ( [18], [19]), rarely the reliability of the devices alone ( [15], [10]). The tools used for this analysis are usually limited to one selected method or algorithm of machine learning.…”