Prediction of Electrical Energy Consumption Using LSTM Algorithm with Teacher Forcing Technique

Electrical energy is an important foundation in world economic growth, therefore it requires an accurate prediction in predicting energy consumption in the future. The methods that are often used in previous research are the Time Series and Machine Learning methods, but recently there has been a new method that can predict energy consumption using the Deep Learning Method which can process data quickly for training and testing. In this research, the researcher proposes a model and algorithm which contained in Deep Learning, that is Multivariate Time Series Model with LSTM Algorithm and using Teacher Forcing Technique for predicting electrical energy consumption in the future. Because Multivariate Time Series Model and LSTM Algorithm can receive input with various conditions or seasons of electrical energy consumption. Teacher Forcing Technique is able lighten up the computation so that it can training and testing data quickly. The method used in this study is to compare Teacher Forcing LSTM with Non-Teacher Forcing LSTM in Multivariate Time Series model using several activation functions that produce significant differences. TF value of RMSE 0.006, MAE 0.070 and Non-TF has RMSE and MAE values of 0.117 and 0.246. The value of the two models is obtained from Sigmoid Activation and the worst value of the two models is in the Softmax activation function, with TF values is RMSE 0.423, MAE 0.485 and Non-TF RMSE 0.520, MAE 0.519.

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Forecasting of Building Electrical Energy Consumption Using Nonlinear Autoregressive Exogenous Input (NARX) Neural Network Time Series (NNTS) Model

Journal of Advanced Research in Dynamical and Control Systems ◽

10.5373/jardcs/v12sp4/20201635 ◽

2020 ◽

Vol 12 (04-Special Issue) ◽

pp. 1555-1560

Author(s):

Widya Kartini Mohd Razali

Keyword(s):

Neural Network ◽

Time Series ◽

Energy Consumption ◽

Electrical Energy ◽

Electrical Energy Consumption ◽

Network Time ◽

Nonlinear Autoregressive ◽

Narx Neural Network

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A Novel Accurate and Fast Converging Deep Learning-Based Model for Electrical Energy Consumption Forecasting in a Smart Grid

Energies ◽

10.3390/en13092244 ◽

2020 ◽

Vol 13 (9) ◽

pp. 2244 ◽

Cited By ~ 2

Author(s):

Ghulam Hafeez ◽

Khurram Saleem Alimgeer ◽

Zahid Wadud ◽

Zeeshan Shafiq ◽

Mohammad Usman Ali Khan ◽

...

Keyword(s):

Deep Learning ◽

Energy Consumption ◽

Convergence Rate ◽

Smart Grid ◽

Energy Management ◽

Electrical Energy ◽

Features Selection ◽

Electrical Energy Consumption ◽

Efficient Energy ◽

Proposed Model

Energy consumption forecasting is of prime importance for the restructured environment of energy management in the electricity market. Accurate energy consumption forecasting is essential for efficient energy management in the smart grid (SG); however, the energy consumption pattern is non-linear with a high level of uncertainty and volatility. Forecasting such complex patterns requires accurate and fast forecasting models. In this paper, a novel hybrid electrical energy consumption forecasting model is proposed based on a deep learning model known as factored conditional restricted Boltzmann machine (FCRBM). The deep learning-based FCRBM model uses a rectified linear unit (ReLU) activation function and a multivariate autoregressive technique for the network training. The proposed model predicts future electrical energy consumption for efficient energy management in the SG. The proposed model is a novel hybrid model comprising four modules: (i) data processing and features selection module, (ii) deep learning-based FCRBM forecasting module, (iii) genetic wind driven optimization (GWDO) algorithm-based optimization module, and (iv) utilization module. The proposed hybrid model, called FS-FCRBM-GWDO, is tested and evaluated on real power grid data of USA in terms of four performance metrics: mean absolute percentage deviation (MAPD), variance, correlation coefficient, and convergence rate. Simulation results validate that the proposed hybrid FS-FCRBM-GWDO model has superior performance than existing models such as accurate fast converging short-term load forecasting (AFC-STLF) model, mutual information-modified enhanced differential evolution algorithm-artificial neural network (MI-mEDE-ANN)-based model, features selection-ANN (FS-ANN)-based model, and Bi-level model, in terms of forecast accuracy and convergence rate.

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Time Series Forecasting with Multi-Headed Attention-Based Deep Learning for Residential Energy Consumption

Energies ◽

10.3390/en13184722 ◽

2020 ◽

Vol 13 (18) ◽

pp. 4722

Author(s):

Seok-Jun Bu ◽

Sung-Bae Cho

Keyword(s):

Time Series ◽

Deep Learning ◽

Energy Consumption ◽

Energy Demand ◽

Multivariate Time Series ◽

Learning Model ◽

Residential Energy ◽

Residential Energy Consumption ◽

Sensor Signals ◽

Deep Learning Model

Predicting residential energy consumption is tantamount to forecasting a multivariate time series. A specific window for several sensor signals can induce various features extracted to forecast the energy consumption by using a prediction model. However, it is still a challenging task because of irregular patterns inside including hidden correlations between power attributes. In order to extract the complicated irregular energy patterns and selectively learn the spatiotemporal features to reduce the translational variance between energy attributes, we propose a deep learning model based on the multi-headed attention with the convolutional recurrent neural network. It exploits the attention scores calculated with softmax and dot product operation in the network to model the transient and impulsive nature of energy demand. Experiments with the dataset of University of California, Irvine (UCI) household electric power consumption consisting of a total 2,075,259 time-series show that the proposed model reduces the prediction error by 31.01% compared to the state-of-the-art deep learning model. Especially, the multi-headed attention improves the prediction performance even more by up to 27.91% than the single-attention.

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Multivariate Time Series Forecasting with Deep Learning Proceedings in Energy Consumption

Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management ◽

10.5220/0008168203840391 ◽

2019 ◽

Author(s):

Nédra Mellouli ◽

Mahdjouba Akerma ◽

Minh Hoang ◽

Denis Leducq ◽

Anthony Delahaye

Keyword(s):

Time Series ◽

Deep Learning ◽

Energy Consumption ◽

Multivariate Time Series ◽

Time Series Forecasting

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Data-Driven Consumption Load Monitoring and Adjustment Strategy in Smart Grid

Journal of Mathematics ◽

10.1155/2021/9373204 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Bingjie He ◽

Jinxiu Xiao ◽

Qiaorong Dai

Keyword(s):

Energy Consumption ◽

Social Welfare ◽

Electrical Energy ◽

Data Driven ◽

Electrical Energy Consumption ◽

Adjustment Strategy ◽

Study Results ◽

The Future ◽

Generation Capacity ◽

Energy Provider

The enhancement of the intelligent construction of the power grid and widespread popularity of smart meters enable large amounts of electrical energy consumption data to be collected and analyzed. Based on the data, the energy provider gives a guiding price in the future periods to users. It encourages users to be more economical and smarter in the process of using electricity. By applying the social welfare model to equate demand and supply in every time interval, we gain the optimal prices and generation capacity. Nevertheless, the truth is that there is a great gap between the consumers’ booked electrical energy consumption and the optimal generation capacity, causing the power system overload and even outage. This article puts forward a novel automatic process control strategy in order to monitor the gap between the consumers’ booked electrical energy consumption and optimal generation capacity by using statistical method to predict the future one. When the predicted value exceeds the boundary, the energy provider adopts the changeable electricity price to stimulate consumers to adjust their electrical energy demands so that it can have smoothly actual electrical energy consumption. Our adjustment method is data-driven exponential function-based adjustment. Case study results show that the strategy can obtain small adjustment times, stable actual consumption load, and controllable prediction errors. Different from the linear monitoring and adjustment strategy, our approach obtains almost the same adjustment frequency, less standard deviation of residuals, and higher total social welfare and energy provider profit.

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