Developing a Mixed Neural Network Approach to Forecast the Residential Electricity Consumption Based on Sensor Recorded Data
| Autor: | Adela Bâra, George Căruțașu, Dana-Mihaela Petroșanu, Simona-Vasilica Oprea, Alexandru Pîrjan, Justina Lavinia Stanica, Cristina Coculescu |
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| Rok vydání: | 2018 |
| Předmět: |
energy management
Operations research function fitting neural network (FITNET) Computer science Energy management 020209 energy Cloud computing 02 engineering and technology Biochemistry Article Analytical Chemistry Internet of Things (IoT) cloud solution 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering residential electricity consumption Instrumentation artificial neural networks (ANNs) forecasting solutions home appliances and devices monitored by sensors Artificial neural network business.industry non-linear autoregressive with exogenous inputs network (NARX) Atomic and Molecular Physics and Optics Renewable energy smart homes Autoregressive model Electricity business |
| Zdroj: | Sensors (Basel, Switzerland) Sensors; Volume 18; Issue 5; Pages: 1443 |
| ISSN: | 1424-8220 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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