Predicting Hourly Energy Consumption. Can Regression Modeling Improve on an Autoregressive Baseline?

Dagnely, Pierre; Ruette, Tom; Tourwé, Tom; Tsiporkova, Elena; Verhelst, Clara

doi:10.1007/978-3-319-27430-0_8

Cited by 14 publications

(22 citation statements)

References 27 publications

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“…], [14], [25], [42], [45], [78], [101], [110], [122], [124], [132], [156], [164], [171], [178], [187], [210], [211], [213], [228], [237], [242], [256]), Support Vector Machines (SVM) (21) ( [? ], [36], [53], [57], [65], [78], [79], [106], [115], [117], [122], [157], [159], [166], [187], [193], [203], [227], [240], [253], [256]), autoregressive integrated moving average (ARIMA) (13) ( [6], [19], [32], [42], [53], [78], [90],…”

Section: Sms Resultsmentioning

confidence: 99%

Smart Grids Data Analysis: A Systematic Mapping Study

Rossi

Chren

2020

IEEE Trans. Ind. Inf.

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Article under review. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.The article is structured as follows. In section II, we provide an overview of Smart Grids concepts: mainly architecture and components of the SG infrastructure. In section III, we define the goals, needs, process and research questions for the Systematic Mapping Study on data analysis in the SG context. In section IV, we present the results from the overall SMS process, divided into answers to the main research questions set. In Section V, we discuss the main threats to validity of the current study. Section VI provides the final conclusions.

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Section: Sms Resultsmentioning

confidence: 99%

Smart Grids Data Analysis: A Systematic Mapping Study

Rossi

Chren

2020

IEEE Trans. Ind. Inf.

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“…They have been used for commercial building load forecasting and for short-term and day-ahead load predictions. Similar to neural networks, these methods do not require any prior knowledge but overcomes that requirement using significant periods of historical training data to produce accurate results [39,41,43,44,46]. In addition, AR models may not perform well in cases where there are high variations in the data [47][48][49].…”

Section: Related Workmentioning

confidence: 99%

“…However, neural networks require significant amount of training data to produce such accurate results [34,36] and hence are not always suited for building load prediction, particularly in newly constructed buildings, where there is not much historical data available. Other algorithms, such as Support Vector Regression [18,19,38,39,43,45], Random Forests [41,42] and Autoregressive models [44,46] have also been used to develop models for building load prediction. They have been used for commercial building load forecasting and for short-term and day-ahead load predictions.…”

Section: Related Workmentioning

confidence: 99%

“…[34][35][36][37][38][39][40][41][42]. These models eliminate the need for extensive prior knowledge about the buildings and users; however, they often require a large amount of training data for each building of interest [34][35][36]39] or result in insufficient prediction accuracy, especially for long-term forecasting (1-5 day ahead forecasting) [37,40,43,44]. Among many, neural networks have been widely used in load forecasting [18,19,29,34,36,37] to obtain accurate predictions of building loads.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Robust Building Energy Load Forecasting Using Physically-Based Kernel Models

Prakash

Rajagopal

et al. 2018

Energies

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Robust and accurate building energy load forecasting is important for helping building managers and utilities to plan, budget, and strategize energy resources in advance. With recent prevalent adoption of smart-meters in buildings, a significant amount of building energy consumption data became available. Many studies have developed physics-based white box models and data-driven black box models to predict building energy consumption; however, they require extensive prior knowledge about building system, need a large set of training data, or lack robustness to different forecasting scenarios. In this paper, we introduce a new building energy forecasting method based on Gaussian Process Regression (GPR) that incorporates physical insights about load data characteristics to improve accuracy while reducing training requirements. The GPR is a non-parametric regression method that models the data as a joint Gaussian distribution with mean and covariance functions and forecast using the Bayesian updating. We model the covariance function of the GPR to reflect the data patterns in different forecasting horizon scenarios, as prior knowledge. Our method takes advantage of the modeling flexibility and computational efficiency of the GPR while benefiting from the physical insights to further improve the training efficiency and accuracy. We evaluate our method with three field datasets from two university campuses (Carnegie Mellon University and Stanford University) for both short-and long-term load forecasting. The results show that our method performs more accurately, especially when the training dataset is small, compared to other state-of-the-art forecasting models (up to 2.95 times smaller prediction error).

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“…The performance of MOGA models was also compared with a NAB model, introduced in [38]. The NAB approach considers, as estimate of the electric power demand at instant k, the measured value of consumption at the corresponding instant of time, in the same day of the previous week.…”

Section: Comparison Of Multi Objective Genetic Algorithm Models Withmentioning

confidence: 99%

A Comparison of Energy Consumption Prediction Models Based on Neural Networks of a Bioclimatic Building

et al. 2016

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Abstract:Energy consumption has been increasing steadily due to globalization and industrialization. Studies have shown that buildings are responsible for the biggest proportion of energy consumption; for example in European Union countries, energy consumption in buildings represents around 40% of the total energy consumption. In order to control energy consumption in buildings, different policies have been proposed, from utilizing bioclimatic architectures to the use of predictive models within control approaches. There are mainly three groups of predictive models including engineering, statistical and artificial intelligence models. Nowadays, artificial intelligence models such as neural networks and support vector machines have also been proposed because of their high potential capabilities of performing accurate nonlinear mappings between inputs and outputs in real environments which are not free of noise. The main objective of this paper is to compare a neural network model which was designed utilizing statistical and analytical methods, with a group of neural network models designed benefiting from a multi objective genetic algorithm. Moreover, the neural network models were compared to a naïve autoregressive baseline model. The models are intended to predict electric power demand at the Solar Energy Research Center (Centro de Investigación en Energía SOLar or CIESOL in Spanish) bioclimatic building located at the University of Almeria, Spain. Experimental results show that the models obtained from the multi objective genetic algorithm (MOGA) perform comparably to the model obtained through a statistical and analytical approach, but they use only 0.8% of data samples and have lower model complexity.

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Predicting Hourly Energy Consumption. Can Regression Modeling Improve on an Autoregressive Baseline?

Cited by 14 publications

References 27 publications

Smart Grids Data Analysis: A Systematic Mapping Study

Smart Grids Data Analysis: A Systematic Mapping Study

Robust Building Energy Load Forecasting Using Physically-Based Kernel Models

A Comparison of Energy Consumption Prediction Models Based on Neural Networks of a Bioclimatic Building

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