Deep learning-based feature engineering methods for improved building energy prediction

Fan, Cheng; Sun, Yongjun; Zhao, Yang; Song, Mengjie; Wang, Jiayuan

doi:10.1016/j.apenergy.2019.02.052

Cited by 217 publications

(81 citation statements)

References 43 publications

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“…The results show that the approach is suitable for assessing DR load shifting options based on a time-of-use pricing scheme achieving district level cost savings of around 15%. In [36], three types of deep learning-based energy-related features are compared with conventional feature engineering methods using fully connected auto-encoders, convolutional autoencoders, and generative adversarial networks. The authors of [35] use deep learning models to predict electricity consumption for arbitrary time horizons, by dividing each predicted sample into a single forecasting sub-problem which is solved independently by identifying the best forecasting model.…”

Section: Related Workmentioning

confidence: 99%

Energy Flexibility Prediction for Data Center Engagement in Demand Response Programs

et al. 2020

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In this paper, we address the problem of the efficient and sustainable operation of data centers (DCs) from the perspective of their optimal integration with the local energy grid through active participation in demand response (DR) programs. For DCs’ successful participation in such programs and for minimizing the risks for their core business processes, their energy demand and potential flexibility must be accurately forecasted in advance. Therefore, in this paper, we propose an energy prediction model that uses a genetic heuristic to determine the optimal ensemble of a set of neural network prediction models to minimize the prediction error and the uncertainty concerning DR participation. The model considers short term time horizons (i.e., day-ahead and 4-h-ahead refinements) and different aspects such as the energy demand and potential energy flexibility (the latter being defined in relation with the baseline energy consumption). The obtained results, considering the hardware characteristics as well as the historical energy consumption data of a medium scale DC, show that the genetic-based heuristic improves the energy demand prediction accuracy while the intra-day prediction refinements further reduce the day-ahead prediction error. In relation to flexibility, the prediction of both above and below baseline energy flexibility curves provides good results for the mean absolute percentage error (MAPE), which is just above 6%, allowing for safe DC participation in DR programs.

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Section: Related Workmentioning

confidence: 99%

Energy Flexibility Prediction for Data Center Engagement in Demand Response Programs

et al. 2020

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“…The validated model was subsequently leveraged for missing value imputation on input time data. Refererence [9] discussed the application of autoencoders and generative networks as a deep-learning alternative to conventional feature engineering in learning models for electrical-energy load forecasting. A method based on Support Vector Regression (SVR) was presented by [10].…”

Section: Related Workmentioning

confidence: 99%

“…To evaluate the prediction models, three performance metrics were used: Mean Squared Error (MSE), Root MSE (RMSE), and Mean Absolute Percentage Error (MAPE). In addition, we included the Coefficient of Variation (CV) of the RMSE based on the evaluation discussed in [9]. The metrics were computed according to the following equations:…”

Section: Experiments Evaluation For Building-energy Time-series Forecamentioning

confidence: 99%

Evaluation of Sequence-Learning Models for Large-Commercial-Building Load Forecasting

et al. 2019

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Buildings play a critical role in the stability and resilience of modern smart grids, leading to a refocusing of large-scale energy-management strategies from the supply side to the consumer side. When buildings integrate local renewable-energy generation in the form of renewable-energy resources, they become prosumers, and this adds more complexity to the operation of interconnected complex energy systems. A class of methods of modelling the energy-consumption patterns of the building have recently emerged as black-box input–output approaches with the ability to capture underlying consumption trends. These make use and require large quantities of quality data produced by nondeterministic processes underlying energy consumption. We present an application of a class of neural networks, namely, deep-learning techniques for time-series sequence modelling, with the goal of accurate and reliable building energy-load forecasting. Recurrent Neural Network implementation uses Long Short-Term Memory layers in increasing density of nodes to quantify prediction accuracy. The case study is illustrated on four university buildings from temperate climates over one year of operation using a reference benchmarking dataset that allows replicable results. The obtained results are discussed in terms of accuracy metrics and computational and network architecture aspects, and are considered suitable for further use in future in situ energy management at the building and neighborhood levels.

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“…First, the raw features with outliers and highdimensional data can make the feature extractions more complex. Therefore, feature preprocessing techniques will be crucial to anomaly detection (reduce outliers) and identify the correlated features to the corresponding forecast time (reduce the dimension) [28]. Second, the single forecasting model may result in large forecasting residuals between the forecast and actual values.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Based Hybrid Framework for Day-Ahead Electricity Price Forecasting

Zhang

2020

IEEE Access

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With the deregulation of the electric energy industry, accurate electricity price forecasting (EPF) is increasingly significant to market participants' bidding strategies and uncertainty risk control. However, it remains a challenging task owing to the high volatility and complicated nonlinearity of electricity prices. Aimed at this, a novel hybrid deep-learning framework is proposed for day-ahead EPF, which includes four modules: the feature preprocessing module, the deep learning-based point prediction module, the error compensation module, and the probabilistic prediction module. The feature preprocessing module is based on isolation forest (IF), and least absolute shrinkage and selection operator (Lasso), which is used to detect outliers and select the correlated features of electricity price series. The point prediction module combines the deep belief network (DBN), long-short-term memory (LSTM) neural network (RNN), and convolutional neural network (CNN), and is employed to extract complicated nonlinear features. The residual error between forecasting price and actual price can be reduced based on the error compensation module. The probabilistic prediction module based on quantile regression (QR) is used to estimate the uncertainty under various confidence levels. The PJM market data is employed in case studies to evaluate the proposed framework, and the results revealed that it has a competitive advantage compared with all of the considered comparison methods. INDEX TERMS Electricity market, day-ahead electricity price forecasting, feature preprocessing, deep learning, error compensation, probabilistic forecasting.

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Deep learning-based feature engineering methods for improved building energy prediction

Cited by 217 publications

References 43 publications

Energy Flexibility Prediction for Data Center Engagement in Demand Response Programs

Energy Flexibility Prediction for Data Center Engagement in Demand Response Programs

Evaluation of Sequence-Learning Models for Large-Commercial-Building Load Forecasting

A Deep Learning Based Hybrid Framework for Day-Ahead Electricity Price Forecasting

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