Load forecasting based on deep neural network and historical data augmentation

Lai, Chun Sing; Mo, Ziwei; Wang, Ting; Yuan, Haoliang; Ng, Wing W. Y.

doi:10.1049/iet-gtd.2020.0842

Cited by 24 publications

(15 citation statements)

References 33 publications

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“…, K centroid load profiles and the corresponding residuals. In order to improve the concatenation method, instead of aggregating all of the historical data from previous years, a historical data augmentation method inserted one feature factor, which adopts adjacent loads as new feature, into the original input [39].…”

Section: Short-term Load Forecastingmentioning

confidence: 99%

An Approach to Data Acquisition for Urban Building Energy Modeling Using a Gaussian Mixture Model and Expectation-Maximization Algorithm

2021

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In recent years, a building’s energy performance is becoming uncertain because of factors such as climate change, the Covid-19 pandemic, stochastic occupant behavior and inefficient building control systems. Sufficient measurement data is essential to predict and manage a building’s performance levels. Assessing energy performance of buildings at an urban scale requires even larger data samples in order to perform an accurate analysis at an aggregated level. However, data are not only expensive, but it can also be a real challenge for communities to acquire large amounts of real energy data. This is despite the fact that inadequate knowledge of a full population will lead to biased learning and the failure to establish a data pipeline. Thus, this paper proposes a Gaussian mixture model (GMM) with an Expectation-Maximization (EM) algorithm that will produce synthetic building energy data. This method is tested on real datasets. The results show that the parameter estimates from the model are stable and close to the true values. The bivariate model gives better performance in classification accuracy. Synthetic data points generated by the models show a consistent representation of the real data. The approach developed here can be useful for building simulations and optimizations with spatio-temporal mapping.

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Section: Short-term Load Forecastingmentioning

confidence: 99%

An Approach to Data Acquisition for Urban Building Energy Modeling Using a Gaussian Mixture Model and Expectation-Maximization Algorithm

2021

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“…The installed power capacity of renewable energy generation grew more than 200 GW, which is mostly PV generation in 2019 [4,5]. However, because of the intermittency and uncertainty of PV, the high penetration of PV could bring great challenges to the power grid, such as power distribution system planning and operation [6][7][8][9], load demand forecasting [10][11][12], hybrid energy system configuration [13,14], and PV power forecasting [15,16].…”

Section: Background and Motivationmentioning

confidence: 99%

“…The delta solar irradiation set ∆R k beh d i , t and approximate delta PV output power set ∆P k * PV,beh d i , t can be calculated by Equations (13) and (14), according to the dates recording in Equations (11) and (12).…”

Section: Pv Output Power Sensitivity Modelmentioning

confidence: 99%

Photovoltaic Output Power Estimation and Baseline Prediction Approach for a Residential Distribution Network with Behind-the-Meter Systems

et al. 2020

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Considering that most of the photovoltaic (PV) data are behind-the-meter (BTM), there is a great challenge to implement effective demand response projects and make a precise customer baseline (CBL) prediction. To solve the problem, this paper proposes a data-driven PV output power estimation approach using only net load data, temperature data, and solar irradiation data. We first obtain the relationship between delta actual load and delta temperature by calculating the delta net load from matching the net load of irradiation for an approximate day with the least squares method. Then we match and make a difference of the net load with similar electricity consumption behavior to establish the relationship between delta PV output power and delta irradiation. Finally, we get the PV output power and implement PV-load decoupling by modifying the relationship between delta PV and delta irradiation. The case studies verify the effectiveness of the approach and it provides an important reference to perform PV-load decoupling and CBL prediction in a residential distribution network with BTM PV systems.

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“…On the other hand, techniques based on machine learning such as artificial neural networks, deep learning, and recurrent neural networks have more complex setup and expensive training-time but they are relatively more accurate and perform better. Among the second-type approaches, the long short-term memory (LSTM) and its newer version named gated recurrent unit (GRU) are very popular techniques and widely used in the recent studies [13,14]. In [14], a deep neural network and historical data augmentation (DNN-HDA) is proposed for data with a high correlation which shows a great improvement in the accuracy.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based load forecasting considering data reshaping using MATLAB\Simulink

Hamad

Abdulrahman

2022

Int J Energy Environ Eng

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Load forecasting is a nonlinear problem and complex task that plays a key role in power system planning, operation, and control. A recent study proposed a deep learning approach called historical data augmentation (HDA) to improve the accuracy of the load forecasting model by dividing the input data into several yearly sub-datasets. When the original data is associated with high time step changes from 1 year to another, the approach was not found as effective as it should be for long-term forecasting because the time-series information is disconnected by the approach between the end of 1-year sub-data and the beginning of the next-year sub-data. Alternatively, this paper proposes the use of 2-year sub-dataset in order to connect the two ends of the yearly subsets. A correlation analysis is conducted to show how the yearly datasets are correlated to each other. In addition, a Simulink-based program is introduced to simulate the problem which has an advantage of visualizing the algorithm. To increase the model generalization, several inputs are considered in the model including load demand profile, weather information, and some important categorical data such as week-day and weekend data that are embedded using onehot encoding technique. The deep learning methods used in this study are the long short-term memory (LSTM) and gated rest unit (GRU) neural networks which have been increasingly employed in the recent years for time series and sequence problems. To provide a theoretical background on these models, a new picturized detail is presented. The proposed method is applied to the Kurdistan regional load demands and compared with classical methods of data inputting demonstrating improvements in both the model accuracy and training time.

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Load forecasting based on deep neural network and historical data augmentation

Cited by 24 publications

References 33 publications

An Approach to Data Acquisition for Urban Building Energy Modeling Using a Gaussian Mixture Model and Expectation-Maximization Algorithm

An Approach to Data Acquisition for Urban Building Energy Modeling Using a Gaussian Mixture Model and Expectation-Maximization Algorithm

Photovoltaic Output Power Estimation and Baseline Prediction Approach for a Residential Distribution Network with Behind-the-Meter Systems

Deep learning-based load forecasting considering data reshaping using MATLAB\Simulink

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