Electricity Consumption Forecasting Based on a Bidirectional Long-Short-Term Memory Artificial Neural Network
| Autor: | Alexandru Pîrjan, Dana-Mihaela Petroșanu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computer science
020209 energy long term electricity consumption forecasting Geography Planning and Development Refrigerator car TJ807-830 Context (language use) 02 engineering and technology 010501 environmental sciences Management Monitoring Policy and Law TD194-195 01 natural sciences Renewable energy sources 0202 electrical engineering electronic engineering information engineering GE1-350 artificial neural networks (ANNs) 0105 earth and related environmental sciences Consumption (economics) Artificial neural network bidirectional long-short-term memory (BiLSTM) networks Environmental effects of industries and plants Renewable Energy Sustainability and the Environment business.industry function fitting neural networks (FITNETs) large commercial center-type consumers Reliability engineering Environmental sciences Key (cryptography) Electricity business Performance metric Efficient energy use |
| Zdroj: | Sustainability, Vol 13, Iss 104, p 104 (2021) Sustainability Volume 13 Issue 1 |
| ISSN: | 2071-1050 |
| Popis: | The accurate forecasting of the hourly month-ahead electricity consumption represents a very important aspect for non-household electricity consumers and system operators, and at the same time represents a key factor in what regards energy efficiency and achieving sustainable economic, business, and management operations. In this context, we have devised, developed, and validated within the paper an hourly month ahead electricity consumption forecasting method. This method is based on a bidirectional long-short-term memory (BiLSTM) artificial neural network (ANN) enhanced with a multiple simultaneously decreasing delays approach coupled with function fitting neural networks (FITNETs). The developed method targets the hourly month-ahead total electricity consumption at the level of a commercial center-type consumer and for the hourly month ahead consumption of its refrigerator storage room. The developed approach offers excellent forecasting results, highlighted by the validation stage&rsquo s results along with the registered performance metrics, namely 0.0495 for the root mean square error (RMSE) performance metric for the total hourly month-ahead electricity consumption and 0.0284 for the refrigerator storage room. We aimed for and managed to attain an hourly month-ahead consumed electricity prediction without experiencing a significant drop in the forecasting accuracy that usually tends to occur after the first two weeks, therefore achieving a reliable method that satisfies the contractor&rsquo s needs, being able to enhance his/her activity from the economic, business, and management perspectives. Even if the devised, developed, and validated forecasting solution for the hourly consumption targets a commercial center-type consumer, based on its accuracy, this solution can also represent a useful tool for other non-household electricity consumers due to its generalization capability. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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