Electricity Consumption Prediction Model Based on Bayesian Regularized BP Neural Network

Chenghao, Yuan; Niu, Dongxiao; Chang-zu, LI; Sun, Lijie; Xu, Longxiu

doi:10.1007/978-3-030-15235-2_76

Cited by 8 publications

(7 citation statements)

References 3 publications

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“…The accuracy measures used throughout this work are the Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE): (27) where y represents the actual value,ŷ the predicted value, and n the number of samples. Note that the MAE and MAPE were calculated with the unnormalized values; the predicted usage vector obtained in the normalized space was converted to the original domain space for the accuracy calculations.…”

Section: Discussionmentioning

confidence: 99%

“…NNs applications for energy forecasting are not new [22]- [24], but as the field of neural networks and deep learning has been evolving fast, so is NN-based forecasting. Jetcheva et al [25] proposed a NN model for day-ahead building-level load forecasting with an ensemble-based approach for parameter selection whereas Chae et al [26] and Yuan et al [27] considered a NN model with Bayesian regularization algorithm. Araya et al [28] proposed an ensemble framework for anomaly detection in building energy consumption; they included prediction-based classifiers (SVR and random forest) as their base forecasting models.…”

Section: A Load Forecastingmentioning

confidence: 99%

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Deep Learning for Load Forecasting: Sequence to Sequence Recurrent Neural Networks With Attention

2020

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The biggest contributor to global warming is energy production and use. Moreover, a push for electrical vehicle and other economic developments are expected to further increase energy use. To combat these challenges, electrical load forecasting is essential as it supports energy production planning and scheduling, assists with budgeting, and helps identify saving opportunities. Machine learning approaches commonly used for energy forecasting such as feedforward neural networks and support vector regression encounter challenges with capturing time dependencies. Consequently, this paper proposes Sequence to Sequence Recurrent Neural Network (S2S RNN) with Attention for electrical load forecasting. The S2S architecture from language translation is adapted for load forecasting and a corresponding sample generation approach is designed. RNN enables capturing time dependencies present in the load data and S2S model further improves time modeling by combining two RNNs: encoder and decoder. The attention mechanism alleviates the burden of connecting encoder and decoder. The experiments evaluated attention mechanisms with different RNN cells (vanilla, LSTM, and GRU) and with varied time horizons. Results show that S2S with Bahdanau attention outperforms other models. Accuracy decreases as forecasting horizon increases; however, longer input sequences do not always increase accuracy. INDEX TERMS Attention mechanism, gated recurrent units, GRU, load forecasting, long short-term memory, LSTM, recurrent neural networks, sequence-to-sequence networks. LJUBISA SEHOVAC (Student Member, IEEE) received the B.Sc. degree in applied math and M.E.Sc. degree in software engineering, collaborative specialization in artificial intelligence

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Section: Discussionmentioning

confidence: 99%

Section: A Load Forecastingmentioning

confidence: 99%

Deep Learning for Load Forecasting: Sequence to Sequence Recurrent Neural Networks With Attention

2020

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“…A one-day-ahead energy prediction scheme is proposed with an ensemble parameter selection model [21]. In [22,23], a Bayesian regularization algorithm is introduced to optimize the neural network prediction scheme. An anomaly detection model based on ensemble neural networks is proposed in [24] to use a random forest to achieve the prediction-based classifiers.…”

Section: Platform Extensions With Deep Learningmentioning

confidence: 99%

Hardware-Based Emulator with Deep Learning Model for Building Energy Control and Prediction Based on Occupancy Sensors’ Data

O’Neill

2021

Information

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Heating, ventilation, and air conditioning (HVAC) is the largest source of residential energy consumption. Occupancy sensors’ data can be used for HVAC control since it indicates the number of people in the building. HVAC and sensors form a typical cyber-physical system (CPS). In this paper, we aim to build a hardware-based emulation platform to study the occupancy data’s features, which can be further extracted by using machine learning models. In particular, we propose two hardware-based emulators to investigate the use of wired/wireless communication interfaces for occupancy sensor-based building CPS control, and the use of deep learning to predict the building energy consumption with the sensor data. We hypothesize is that the building energy consumption may be predicted by using the occupancy data collected by the sensors, and question what type of prediction model should be used to accurately predict the energy load. Another hypothesis is that an in-lab hardware/software platform could be built to emulate the occupancy sensing process. The machine learning algorithms can then be used to analyze the energy load based on the sensing data. To test the emulator, the occupancy data from the sensors is used to predict energy consumption. The synchronization scheme between sensors and the HVAC server will be discussed. We have built two hardware/software emulation platforms to investigate the sensor/HVAC integration strategies, and used an enhanced deep learning model—which has sequence-to-sequence long short-term memory (Seq2Seq LSTM)—with an attention model to predict the building energy consumption with the preservation of the intrinsic patterns. Because the long-range temporal dependencies are captured, the Seq2Seq models may provide a higher accuracy by using LSTM architectures with encoder and decoder. Meanwhile, LSTMs can capture the temporal and spatial patterns of time series data. The attention model can highlight the most relevant input information in the energy prediction by allocating the attention weights. The communication overhead between the sensors and the HVAC control server can also be alleviated via the attention mechanism, which can automatically ignore the irrelevant information and amplify the relevant information during CNN training. Our experiments and performance analysis show that, compared with the traditional LSTM neural network, the performance of the proposed method has a 30% higher prediction accuracy.

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“…BP network has become one of the most widely used neural network models because of its strong nonlinear mapping ability and self-learning ability [16][17][18]. However, it has some problems, such as slow convergence speed, lack of scientific theoretical basis for the determination of hidden layer nodes, and easily falling into local minimum points [19]. Deep learning is a branch of neural networks.…”

Section: Introductionmentioning

confidence: 99%

An Attention-BiLSTM Model for Satellite Operation Prediction with Correlation Telemetry

Peng

Jia

Xie

et al. 2023

Preprint

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At present, satellite anomaly is mostly solved after the event, and rarely predicted in advance in satellite health management. Thus, satellite trend prediction is quietly important for avoiding the fault which perhaps affects data accuracy and service quality of satellite, and even impacts greatly on satellite safety. However, it is difficult to predict satellite operation through a simple model because satellite system is complex, and telemetry data is numerous, coupled and spatiotemporal. Therefore, this paper proposes a model combing attention mechanism and Bidirectional Long Short-term Memory (Attention-BiLSTM) with correlation telemetry to predict the situation of satellite operation. Firstly, high-dimensional K-NearestNeighbor Mutual Information (HKNN-MI) method is performed to select the related telemetry variables from multiple variables of satellite telemetry data. Secondly, we put forward to a new BiLSTM model with attention mechanism for telemetry prediction. The dataset for the research is generated and transmitted from the power system of FY3E meteorological satellite. In order to verify the superiority of the proposed model, it is compared with other method based on the same dataset in the experiment. The result shows that the method outperforms other methods due to its better accuracy and prediction precision.

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Electricity Consumption Prediction Model Based on Bayesian Regularized BP Neural Network

Cited by 8 publications

References 3 publications

Deep Learning for Load Forecasting: Sequence to Sequence Recurrent Neural Networks With Attention

Deep Learning for Load Forecasting: Sequence to Sequence Recurrent Neural Networks With Attention

Hardware-Based Emulator with Deep Learning Model for Building Energy Control and Prediction Based on Occupancy Sensors’ Data

An Attention-BiLSTM Model for Satellite Operation Prediction with Correlation Telemetry

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