Day-ahead building-level load forecasts using deep learning vs. traditional time-series techniques

Cai, Mengmeng; Pipattanasomporn, Manisa; Rahman, Saifur

doi:10.1016/j.apenergy.2018.12.042

Cited by 379 publications

(121 citation statements)

References 33 publications

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“…Although this model could achieve satisfactory performance with a low average mean absolute percentage error, it was developed to forecast loads at the building level including several customers. The work in [27] also presented a more accurate method based on deep learning algorithms for day-ahead building level load forecasts.…”

Section: Load Forecasting In Distribution Systemsmentioning

confidence: 99%

Single Residential Load Forecasting Using Deep Learning and Image Encoding Techniques

Estebsari

Rajabi

2020

Electronics

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The integration of more renewable energy resources into distribution networks makes the operation of these systems more challenging compared to the traditional passive networks. This is mainly due to the intermittent behavior of most renewable resources such as solar and wind generation. There are many different solutions being developed to make systems flexible such as energy storage or demand response. In the context of demand response, a key factor is to estimate the amount of load over time properly to better manage the demand side. There are many different forecasting methods, but the most accurate solutions are mainly found for the prediction of aggregated loads at the substation or building levels. However, more effective demand response from the residential side requires prediction of energy consumption at every single household level. The accuracy of forecasting loads at this level is often lower with the existing methods as the volatility of single residential loads is very high. In this paper, we present a hybrid method based on time series image encoding techniques and a convolutional neural network. The results of the forecasting of a real residential customer using different encoding techniques are compared with some other existing forecasting methods including SVM, ANN, and CNN. Without CNN, the lowest mean absolute percentage of error (MAPE) for a 15 min forecast is above 20%, while with existing CNN, directly applied to time series, an MAPE of around 18% could be achieved. We find the best image encoding technique for time series, which could result in higher accuracy of forecasting using CNN, an MAPE of around 12%.

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Section: Load Forecasting In Distribution Systemsmentioning

confidence: 99%

Single Residential Load Forecasting Using Deep Learning and Image Encoding Techniques

Estebsari

Rajabi

2020

Electronics

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“…For simplicity, from hereon, we drop the dependence on θ in m(y t−1 , z P t ; θ) and use the shorthand notation m(y t−1 , z P t ). The recursive prediction consists of repeating one-step-ahead prediction several times using the previous forecast as input [11]. We compute forecasts recursively for h = 0, .…”

Section: Inputs Selection and Model Architecturesmentioning

confidence: 99%

“…In [11], historical data for one year period, with 5% of missing data, were used to predict consumption on weekdays only. Missing values were handled by listwise deletion.…”

Section: Introductionmentioning

confidence: 99%

Robust and automatic data cleansing method for short-term load forecasting of distribution feeders

et al. 2020

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Distribution networks are undergoing fundamental changes at medium voltage level. To support growing planning and control decision-making, the need for large numbers of short-term load forecasts has emerged. Data-driven modelling of medium voltage feeders can be affected by (1) data quality issues, namely, large gross errors and missing observations (2) the presence of structural breaks in the data due to occasional network reconfiguration and load transfers. The present work investigates and reports on the effects of advanced data cleansing techniques on forecast accuracy. A hybrid framework to detect and remove outliers in large datasets is proposed; this automatic procedure combines the Tukey labelling rule and the binary segmentation algorithm to cleanse data more efficiently, it is fast and easy to implement. Various approaches for missing value imputation are investigated, including unconditional mean, Hot Deck via k-nearest neighbour and Kalman smoothing. A combination of the automatic detection/removal of outliers and the imputation methods mentioned above are implemented to cleanse time series of 342 medium-voltage feeders. A nested rolling-origin-validation technique is used to evaluate the feed-forward deep neural network models. The proposed data cleansing framework efficiently removes outliers from the data, and the accuracy of forecasts is improved. It is found that Hot Deck (k-NN) imputation performs best in balancing the bias-variance trade-off for short-term forecasting.

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“…Recent works demonstrate that neural networks with convolution operations can achieve top performance in sequence tasks such as speech synthesis, language modeling, 4 Complexity and machine translation [42][43][44]. CNN-based networks are also used for load forecasting problems in [45][46][47]. Based on the analysis of residential load forecasting, we propose a unified quantile regression deep neural network with timecognition, which consists of sequence-to-sequence (S2S) multi-scale CNN structure (MS-CNN), periodic time coding, and quantile regression components.…”

Section: Problem Identificationmentioning

confidence: 99%

Unified Quantile Regression Deep Neural Network with Time-Cognition for Probabilistic Residential Load Forecasting

et al. 2020

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Residential load forecasting is important for many entities in the electricity market, but the load profile of single residence shows more volatilities and uncertainties. Due to the difficulty in producing reliable point forecasts, probabilistic load forecasting becomes more popular as a result of catching the volatility and uncertainty by intervals, density, or quantiles. In this paper, we propose a unified quantile regression deep neural network with time-cognition for tackling this challenging issue. At first, a convolutional neural network with multiscale convolution is devised for extracting more behavioral features from the historical load sequence. In addition, a novel periodical coding method marks the model to enhance its ability of capturing regular load pattern. Then, features generated from both subnetworks are fused and fed into the forecasting model with an end-to-end manner. Besides, a globally differentiable quantile loss function constrains the whole network for training. At last, forecasts of multiple quantiles are directly generated in one shot. With ablation experiments, the proposed model achieved the best results in the AQS, AACE, and inversion error, and especially the average of the AACE is grown by 34.71%, 75.22%, and 32.44% compared with QGBRT, QCNN, and QLSTM, respectively, indicating that our method has excellent reliability and robustness rather than the state-of-the-art models obviously. Meanwhile, great performances of efficient time response demonstrate that our proposed work has promising prospects in practical applications.

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Day-ahead building-level load forecasts using deep learning vs. traditional time-series techniques

Cited by 379 publications

References 33 publications

Single Residential Load Forecasting Using Deep Learning and Image Encoding Techniques

Single Residential Load Forecasting Using Deep Learning and Image Encoding Techniques

Robust and automatic data cleansing method for short-term load forecasting of distribution feeders

Unified Quantile Regression Deep Neural Network with Time-Cognition for Probabilistic Residential Load Forecasting

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