A long short-term memory artificial neural network to predict daily HVAC consumption in buildings

2020 ◽  
Vol 216 ◽  
pp. 109952 ◽  
Author(s):  
R. Sendra-Arranz ◽  
A. Gutiérrez
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Artificial Neural

scholarly journals INSTALLED SOLAR POWER PREDICTION FOR TURKEY USING ARTIFICIAL NEURAL NETWORK AND BIDIRECTIONAL LONG SHORT-TERM MEMORY

2020 ◽  
Vol 8 (5) ◽  
pp. 4047-4068
Author(s):  
Mehmet Hakan ÖZDEMİR ◽  
Murat İNCE ◽  
Batin Latif AYLAK ◽  
Okan ORAL ◽  
Mehmet Ali TAŞ
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Renewable Energy ◽  
Solar Power ◽  
Short Term Memory ◽  
Renewable Energy Sources ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Artificial Neural

Renewable energy sources play an essential role in sustainable development. The share of renewable energy-based energy generation is rapidly increasing all over the world. Turkey has a great potential in terms of both solar and wind energy due to its geographical location. The desired level has not yet been reached in using this potential. Nevertheless, with the increase in installed power in recent years, electricity generation from solar energy has gained momentum. In this study, data on cumulative installed solar power in Turkey in the 2009-2019 period were used. Artificial Neural Network (ANN) and Bidirectional Long Short-Term Memory (BLSTM) methods were selected to predict the cumulative installed solar power for 2020 with these data. The cumulative installed power was predicted, and the results were compared and interpreted.

scholarly journals A Particulate Matter Concentration Prediction Model Based on Long Short-Term Memory and an Artificial Neural Network

2021 ◽  
Vol 18 (13) ◽  
pp. 6801
Author(s):  
Junbeom Park ◽  
Seongju Chang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Particulate Matter ◽  
Industrial Development ◽  
Short Term Memory ◽  
Model Performance ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Artificial Neural

Many countries are concerned about high particulate matter (PM) concentrations caused by rapid industrial development, which can harm both human health and the environment. To manage PM, the prediction of PM concentrations based on historical data is actively being conducted. Existing technologies for predicting PM mostly assess the model performance for the prediction of existing PM concentrations; however, PM must be forecast in advance, before it becomes highly concentrated and causes damage to the citizens living in the affected regions. Thus, it is necessary to conduct research on an index that can illustrate whether the PM concentration will increase or decrease. We developed a model that can predict whether the PM concentration might increase or decrease after a certain time, specifically for PM2.5 (fine PM) generated by anthropogenic volatile organic compounds. An algorithm that can select a model on an hourly basis, based on the long short-term memory (LSTM) and artificial neural network (ANN) models, was developed. The proposed algorithm exhibited a higher F1-score than the LSTM, ANN, or random forest models alone. The model developed in this study could be used to predict future regional PM concentration levels more effectively.

scholarly journals Multiple-Depth Soil Moisture Estimates Using Artificial Neural Network and Long Short-Term Memory Models

Water ◽  
2021 ◽  
Vol 13 (18) ◽  
pp. 2584
Author(s):  
Heechan Han ◽  
Changhyun Choi ◽  
Jongsung Kim ◽  
Ryan R. Morrison ◽  
Jaewon Jung ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Moisture ◽  
Short Term Memory ◽  
Data Driven ◽  
Memory Model ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Artificial Neural

Accurate prediction of soil moisture is important yet challenging in various disciplines, such as agricultural systems, hydrology studies, and ecosystems studies. However, many data-driven models are being used to simulate and predict soil moisture at only a single depth. To predict soil moisture at various soil depths with depths of 100, 200, 500, and 1000 mm from the surface, based on the weather and soil characteristic data, this study designed two data-driven models: artificial neural networks and long short-term memory models. The developed models are applied to predict daily soil moisture up to 6 days ahead at four depths in the Eagle Lake Observatory in California, USA. The overall results showed that the long short-term memory model provides better predictive performance than the artificial neural network model for all depths. The root mean square error of the predicted soil moisture from both models is lower than 2.0, and the correlation coefficient is 0.80–0.97 for the artificial neural network model and 0.90–0.98 for the long short-term memory model. In addition, monthly based evaluation results showed that soil moisture predicted from the data-driven models is highly useful for analyzing the effects on the water cycle during the wet season as well as dry seasons. The prediction results can be used as basic data for numerous fields such as hydrological study, agricultural study, and environment, respectively.

scholarly journals Electricity Consumption Forecasting Based on a Bidirectional Long-Short-Term Memory Artificial Neural Network

2020 ◽  
Vol 13 (1) ◽  
pp. 104
Author(s):  
Dana-Mihaela Petroșanu ◽  
Alexandru Pîrjan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Short Term Memory ◽  
Electricity Consumption ◽  
Short Term ◽  
Term Memory ◽  
Storage Room ◽  
Long Short Term Memory ◽  
Commercial Center ◽  
Business And Management

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’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’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.

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