Data-driven heating and cooling load predictions for non-residential buildings based on support vector machine regression and NARX Recurrent Neural Network: A comparative study on district scale

Koschwitz, Daniel; Frisch, Jérôme; Treeck, Christoph van

doi:10.1016/j.energy.2018.09.068

Cited by 148 publications

(65 citation statements)

References 24 publications

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“…Their prediction accuracy was 98.7% in the training period and 97.6% in the testing period. Koschwitz et al [9] evaluated monthly load predictions using data-driven thermal loads with two nonlinear autoregressive exogenous recurrent neural networks (NARX RNNs) at different depths and a linear epsilon-insensitive support vector machine (ε-SVM) regression model. Their results indicate that the NARX RNN method provides more accurate predictions than the ε-SVM regression model.…”

Section: Introductionmentioning

confidence: 99%

Cooling Load Forecasting via Predictive Optimization of a Nonlinear Autoregressive Exogenous (NARX) Neural Network Model

2019

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Accurate calculations and predictions of heating and cooling loads in buildings play an important role in the development and implementation of building energy management plans. This study aims to improve the forecasting accuracy of cooling load predictions using an optimized nonlinear autoregressive exogenous (NARX) neural network model. The preprocessing of training data and optimization of parameters were investigated for model optimization. In predictive models of cooling loads, the removal of missing values and the adjustment of structural parameters have been shown to help improve the predictive performance of a neural network model. In this study, preprocessing the training data eliminated missing values for times when the heating, ventilation, and air-conditioning system is not running. Also, the structural and learning parameters were adjusted to optimize the model parameters.

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Section: Introductionmentioning

confidence: 99%

Cooling Load Forecasting via Predictive Optimization of a Nonlinear Autoregressive Exogenous (NARX) Neural Network Model

2019

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“…In [39], authors propose a comparison of two data-driven models for thermal load forecasting. The first model is based on support vector machines exploiting a radial basis function and a polynomial kernel.…”

Section: Energy Domain Applicationsmentioning

confidence: 99%

“…However, such works lack in the integration of real data and none of the proposed methodologies integrates IoT devices and metering infrastructure, hence limiting the applicability in future smart cities for both planning and operational phases. Finally, the works [31,[34][35][36][37][38][39][40][41] propose either clustering [41], or fault location [31], or prediction techniques [34][35][36][37][38][39][40] at the building [31,[34][35][36][37] or at the district scale [38][39][40][41]. Such solutions do not provide an integrated and distributed system able to collect a large volume of energy-related data and efficiently compute both characterization and forecasting of energy consumption, applied in a real-world use-case scenario.…”

Section: Comparison and Contributions Of The Current Workmentioning

confidence: 99%

Forecasting Heating Consumption in Buildings: A Scalable Full-Stack Distributed Engine

et al. 2019

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Predicting power demand of building heating systems is a challenging task due to the high variability of their energy profiles. Power demand is characterized by different heating cycles including sequences of various transient and steady-state phases. To effectively perform the predictive task by exploiting the huge amount of fine-grained energy-related data collected through Internet of Things (IoT) devices, innovative and scalable solutions should be devised. This paper presents PHi-CiB, a scalable full-stack distributed engine, addressing all tasks from energy-related data collection, to their integration, storage, analysis, and modeling. Heterogeneous data measurements (e.g., power consumption in buildings, meteorological conditions) are collected through multiple hardware (e.g., IoT devices) and software (e.g., web services) entities. Such data are integrated and analyzed to predict the average power demand of each building for different time horizons. First, the transient and steady-state phases characterizing the heating cycle of each building are automatically identified; then the power-level forecasting is performed for each phase. To this aim, PHi-CiB relies on a pipeline of three algorithms: the Exponentially Weighted Moving Average, the Multivariate Adaptive Regression Spline, and the Linear Regression with Stochastic Gradient Descent. PHi-CiB’s current implementation exploits Apache Spark and MongoDB and supports parallel and scalable processing and analytical tasks. Experimental results, performed on energy-related data collected in a real-world system show the effectiveness of PHi-CiB in predicting heating power consumption of buildings with a limited prediction error and an optimal horizontal scalability.

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“…In [30] a prediction model for district energy consumption in medium (mothly) and long terms (yearly) is presented, based on an ensamble of three different data-mining techniques. In [31] two data-driven thermal-load forecasting models are compared, one based on support vector machines, and another with two nonlinear autoregressive exogenous recurrent neural networks. In [32], two heating distribution network substations in Changchun, China, were analyzed by means of data mining techniques.…”

Section: Related Workmentioning

confidence: 99%

Exploiting Scalable Machine-Learning Distributed Frameworks to Forecast Power Consumption of Buildings

2019

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The pervasive and increasing deployment of smart meters allows collecting a huge amount of fine-grained energy data in different urban scenarios. The analysis of such data is challenging and opening up a variety of interesting and new research issues across energy and computer science research areas. The key role of computer scientists is providing energy researchers and practitioners with cutting-edge and scalable analytics engines to effectively support their daily research activities, hence fostering and leveraging data-driven approaches. This paper presents SPEC, a scalable and distributed engine to predict building-specific power consumption. SPEC addresses the full analytic stack and exploits a data stream approach over sliding time windows to train a prediction model tailored to each building. The model allows us to predict the upcoming power consumption at a time instant in the near future. SPEC integrates different machine learning approaches, specifically ridge regression, artificial neural networks, and random forest regression, to predict fine-grained values of power consumption, and a classification model, the random forest classifier, to forecast a coarse consumption level. SPEC exploits state-of-the-art distributed computing frameworks to address the big data challenges in harvesting energy data: the current implementation runs on Apache Spark, the most widespread high-performance data-processing platform, and can natively scale to huge datasets. As a case study, SPEC has been tested on real data of an heating distribution network and power consumption data collected in a major Italian city. Experimental results demonstrate the effectiveness of SPEC to forecast both fine-grained values and coarse levels of power consumption of buildings.

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Data-driven heating and cooling load predictions for non-residential buildings based on support vector machine regression and NARX Recurrent Neural Network: A comparative study on district scale

Cited by 148 publications

References 24 publications

Cooling Load Forecasting via Predictive Optimization of a Nonlinear Autoregressive Exogenous (NARX) Neural Network Model

Cooling Load Forecasting via Predictive Optimization of a Nonlinear Autoregressive Exogenous (NARX) Neural Network Model

Forecasting Heating Consumption in Buildings: A Scalable Full-Stack Distributed Engine

Exploiting Scalable Machine-Learning Distributed Frameworks to Forecast Power Consumption of Buildings

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