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

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

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

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

Abstract and Assessment of Doctoral Theses(2005)

Principles of Innovation Uncertainty Management and the IUM Model(イノベーションの不確実性マネジメントの原理とモデル)[Klasen]公共財供給の経済分析 : 選挙と政治献金の経済モデル[金崎]環境規制と汚染者の有限責任問題の経済分析[境]研究開発活動と内生的成長に関する理論分析 : マクロ動学モデルによる分析[片桐]台湾パソコン産業の発展とグローバル生産ネットワーク : 学習という観点から[中原]A REVIEW OF THE KNOWLEDGE MANAGEMENT MODEL BASED ON AN EMPIRICAL SURVEY OF KOREAN EXPERTS(韓国専門家実証調査による知識経営モデル考察)[朴]金融機関と金融行政の効率性の経済分析[下田]分散共分散変動モデルによる現物資産価格変動のリスクヘッジ[森田]産業廃棄物税の研究 : わが国における分権的環境政策の観点から[金子]アメリカにおけるミクロ社会経済分析の方法的形成とわが国への適用[伊藤]新規株式公開（IPO）制度と利益マネジメント : 新規公開企業の業績・株価動向を中心に[松本]私立大学のガバナンス : 統治と意思決定の仕組み[平山