{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T14:07:53Z","timestamp":1762956473439,"version":"3.40.5"},"reference-count":40,"publisher":"Wiley","license":[{"start":{"date-parts":[[2020,12,12]],"date-time":"2020-12-12T00:00:00Z","timestamp":1607731200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Internet of Things Integrated Innovation and Integration","award":["2018-470","2019LZ-06"],"award-info":[{"award-number":["2018-470","2019LZ-06"]}]},{"name":"Key Fund of Shaanxi Province Natural Science Basic Research Program","award":["2018-470","2019LZ-06"],"award-info":[{"award-number":["2018-470","2019LZ-06"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Wireless Communications and Mobile Computing"],"published-print":{"date-parts":[[2020,12,12]]},"abstract":"<jats:p>Model predictive control is theoretically suitable for optimal control of the building, which provides a framework for optimizing a given cost function (e.g., energy consumption) subject to constraints (e.g., thermal comfort violations and HVAC system limitations) over the prediction horizon. However, due to the buildings\u2019 heterogeneous nature, control-oriented physical models\u2019 development may be cost and time prohibitive. Data-driven predictive control, integration of the \u201cInternet of Things\u201d, provides an attempt to bypass the need for physical modeling. This work presents an innovative study on a data-driven predictive control (DPC) for building energy management under the four-tier building energy Internet of Things architecture. Here, we develop a cloud-based SCADA building energy management system framework for the standardization of communication protocols and data formats, which is favorable for advanced control strategies implementation. Two DPC strategies based on building predictive models using the regression tree (RT) and the least-squares boosting (LSBoost) algorithms are presented, which are highly interpretable and easy for different stakeholders (end-user, building energy manager, and\/or operator) to operate. The predictive model\u2019s complexity is reduced by efficient feature selection to decrease the variables\u2019 dimensionality and further alleviate the DPC optimization problem\u2019s complexity. The selection is dependent on the principal component analysis (PCA) and the importance of disturbance variables (IoD). The proposed strategies are demonstrated both in residential and office buildings. The results show that the DPC-LSBoost has outperformed the DPC-RT and other existing control strategies (MPC, TDNN) in performance, scalability, and robustness.<\/jats:p>","DOI":"10.1155\/2020\/8849541","type":"journal-article","created":{"date-parts":[[2020,12,15]],"date-time":"2020-12-15T12:00:31Z","timestamp":1608033631000},"page":"1-20","source":"Crossref","is-referenced-by-count":18,"title":["Data-Driven Predictive Control of Building Energy Consumption under the IoT Architecture"],"prefix":"10.1155","volume":"2020","author":[{"given":"Ji","family":"Ke","sequence":"first","affiliation":[{"name":"College of Electronic & Control engineering Chang\u2019an University, Xi\u2019an 710061, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3443-2130","authenticated-orcid":true,"given":"Yude","family":"Qin","sequence":"additional","affiliation":[{"name":"College of Electronic & Control engineering Chang\u2019an University, Xi\u2019an 710061, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2495-4506","authenticated-orcid":true,"given":"Biao","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Electronic & Control engineering Chang\u2019an University, Xi\u2019an 710061, China"}]},{"given":"Shundong","family":"Yang","sequence":"additional","affiliation":[{"name":"KunYi Co., Ltd., Wuxi 214000, China"}]},{"given":"Hao","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Electronic & Control engineering Chang\u2019an University, Xi\u2019an 710061, China"}]},{"given":"Hang","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Electronic & Control engineering Chang\u2019an University, Xi\u2019an 710061, China"}]},{"given":"Xing","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Electronic & Control engineering Chang\u2019an University, Xi\u2019an 710061, China"}]}],"member":"311","reference":[{"volume-title":"A three-tier architecture visual-programming platform for building-lifecycle data management","year":"2020","author":"M. 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