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Journal : TELKOMNIKA (Telecommunication Computing Electronics and Control)

Sub-1 GHz Wireless Nodes Performance Evaluation for Intelligent Greenhouse System I Nyoman Kusuma Wardana; Ngakan Nyoman Kutha Krisnawijaya; I Wayan Aditya Suranata
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 6: December 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i6.11556

Abstract

Greenhouses provide not only solution to problems faced by conventional farming systems but also play an important role to improve the energy efficiency and environmentally friendly awareness. To achieve benefits of greenhouse farming system in terms of energy efficiency, research related to this issue have been done by many researchers. However, resources that concern on how to practically implement the particular energy-saving technology for greenhouses need to be improved. In this research, field experiment results related to low-power communication between nodes have been reported by implementing universal prototype modules. The pros and cons of existing communication technology, the proposed architecture of network and module analysis, and the performance evaluation of the proposed module dedicated to intelligent greenhouse farming system were also discussed.
Feature engineering and long short-term memory for energy use of appliances prediction I Wayan Aditya Suranata; I Nyoman Kusuma Wardana; Naser Jawas; I Komang Agus Ady Aryanto
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 3: June 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i3.17882

Abstract

Electric energy consumption in a residential household is one of the key factors that affect the overall national electricity demand. Household appliances are one of the most electricity consumers in a residential household. Therefore, it is crucial to make a proper prediction for the electricity consumption of these appliances. This research implemented feature engineering technique and long short-term memory (LSTM) as a model predictor. Principal component analysis (PCA) was implemented as a feature extractor by reducing the final 62 features to 25 principal components for the LSTM inputs. Based on the experiments, the two-layered LSTM model (composed by 25 and 20 neurons for the first and second later respectively) with lookback number of 3 found to give the best performance with the error rates of 62.013 and 26.982 for RMSE and MAE, respectively.