Lambda-Based Data Processing Architecture for Two-Level Load Forecasting in Residential Buildings

Gde Dharma Nugraha, Ardiansyah Musa, Jaiyoung Cho, Kishik Park and Deokjai Choi
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Gde Dharma Nugraha: Advanced Network Lab, School of Electronics and Computer Engineering, Chonnam National University, Gwangju 61186, Korea
Ardiansyah Musa: Advanced Network Lab, School of Electronics and Computer Engineering, Chonnam National University, Gwangju 61186, Korea
Jaiyoung Cho: Wonkwang Electric Power Co., 243 Haenamhwasan-ro, Haenam-gun, Chonnam 59046, Korea
Kishik Park: BonC Innovators Co., 26 Jeongbohwa-gil, Naju-city, Chonnam 58217, Korea
Deokjai Choi: Advanced Network Lab, School of Electronics and Computer Engineering, Chonnam National University, Gwangju 61186, Korea

Energies, 2018, vol. 11, issue 4, 1-20

Abstract: Building energy management systems (BEMS) have been intensively used to manage the electricity consumption of residential buildings more efficiently. However, the dynamic behavior of the occupants introduces uncertainty problems that affect the performance of the BEMS. To address this uncertainty problem, the BEMS may implement load forecasting as one of the BEMS modules. Load forecasting utilizes historical load data to compute model predictions for a specific time in the future. Recently, smart meters have been introduced to collect electricity consumption data. Smart meters not only capture aggregation data, but also individual data that is more frequently close to real-time. The processing of both smart meter data types for load forecasting can enhance the performance of the BEMS when confronted with uncertainty problems. The collection of smart meter data can be processed using a batch approach for short-term load forecasting, while the real-time smart meter data can be processed for very short-term load forecasting, which adjusts the short-term load forecasting to adapt to the dynamic behavior of the occupants. This approach requires different data processing techniques for aggregation and individual of smart meter data. In this paper, we propose Lambda-based data processing architecture to process the different types of smart meter data and implement the two-level load forecasting approach, which combines short-term and very short-term load forecasting techniques on top of our proposed data processing architecture. The proposed approach is expected to enhance the BEMS to address the uncertainty problem in order to process data in less time. Our experiment showed that the proposed approaches improved the accuracy by 7% compared to a typical BEMS with only one load forecasting technique, and had the lowest computation time when processing the smart meter data.

Keywords: load forecasting; building energy management systems (BEMS); lambda architecture; two-level load forecasting; short-term load forecasting (STLF); scheduler (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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