Uni-Variate and Multi-Variate Short-Term Household Electricity Consumption Prediction Using Machine Learning Technique

2021 ◽  
Vol 14 ◽  
Author(s):  
Sakshi Tyagi ◽  
Pratima Singh
Keyword(s):  
Prediction Model ◽  
Multivariate Time Series ◽  
Real Life ◽  
Electricity Consumption ◽  
Short Term ◽  
Resource Saving ◽  
Current Time ◽  
Time Series Dataset ◽  
Lstm Network ◽  
Consumption Prediction

Background: Electricity consumption prediction plays an important role in conservation, development, and future planning. Accurate prediction model has various field applications in real-life scenarios, future electricity demand estimation, performance evaluation of current time, fault detection, efficient energy production, resource-saving, and many more. In this paper, a CNN based short term building electricity consumption prediction model is developed and tested for two different types of datasets that can perform weekly prediction. Two different datasets are used to check how the algorithm behaves on different datasets i.e., what are the impacts dataset has on prediction accuracy. Errors were calculated using MAE and RMSE. Objective: The objective of the study is to develop an electricity consumption prediction (ECP) model for a univariate and multivariate dataset using CNN and LSTM network and to find that how the correlation and independency of features affect the electricity prediction task. Methods: The proposed electricity consumption model is built using the deep CNN andLSTM network and is trained and tested using the univariate and multivariate time series dataset thus the two experiments have been performed and are named as U-ECPCL (Univariate- Electricity Consumption Prediction using CNN and LSTM) and M-ECPCL (Multivariate- Electricity Consumption Prediction using CNN and LSTM) respectively. Results: The model predicts accurately with few errors with MAE of 0.251 and RMSE of 0.66 for univariate dataset and MAE of 4.36 and RMSE of 11.53 for a multivariate dataset. Conclusion: The model predicts accurately with few errors and if the prediction error of univariate and multivariate are compared then it is concluded that the univariate model outperforms the multivariate model.

scholarly journals Short-Term Electricity Consumption Forecasting Based on the EMD-Fbprophet-LSTM Method

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Guorong Zhu ◽  
Sha Peng ◽  
Yongchang Lao ◽  
Qichao Su ◽  
Qiujie Sun
Keyword(s):  
Time Series ◽  
Power Consumption ◽  
Prediction Model ◽  
Time Series Prediction ◽  
Electricity Consumption ◽  
Operating Efficiency ◽  
Short Term ◽  
Model Based ◽  
Consumption Data ◽  
Consumption Prediction

Short-term electricity consumption data reflects the operating efficiency of grid companies, and accurate forecasting of electricity consumption helps to achieve refined electricity consumption planning and improve transmission and distribution transportation efficiency. In view of the fact that the power consumption data is nonstationary, nonlinear, and greatly influenced by the season, holidays, and other factors, this paper adopts a time-series prediction model based on the EMD-Fbprophet-LSTM method to make short-term power consumption prediction for an enterprise's daily power consumption data. The EMD model was used to decompose the time series into a multisong intrinsic mode function (IMF) and a residual component, and then the Fbprophet method was used to predict the IMF component. The LSTM model is used to predict the short-term electricity consumption, and finally the prediction value of the combined model is measured based on the weights of the single Fbprophet and LSTM models. Compared with the single time-series prediction model, the time-series prediction model based on the EMD-Fbprophet-LSTM method has higher prediction accuracy and can effectively improve the accuracy of short-term regional electricity consumption prediction.

scholarly journals A GreyNGM(1,1,k)Self-Memory Coupling Prediction Model for Energy Consumption Prediction

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaojun Guo ◽  
Sifeng Liu ◽  
Lifeng Wu ◽  
Lingling Tang
Keyword(s):  
Energy Consumption ◽  
Prediction Model ◽  
Electricity Consumption ◽  
Predictive Performance ◽  
Energy System ◽  
The Self ◽  
Grey Prediction ◽  
Stochastic Fluctuation ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

Energy consumption prediction is an important issue for governments, energy sector investors, and other related corporations. Although there are several prediction techniques, selection of the most appropriate technique is of vital importance. As for the approximate nonhomogeneous exponential data sequence often emerging in the energy system, a novel greyNGM(1,1,k)self-memory coupling prediction model is put forward in order to promote the predictive performance. It achieves organic integration of the self-memory principle of dynamic system and greyNGM(1,1,k)model. The traditional grey model’s weakness as being sensitive to initial value can be overcome by the self-memory principle. In this study, total energy, coal, and electricity consumption of China is adopted for demonstration by using the proposed coupling prediction technique. The results show the superiority ofNGM(1,1,k)self-memory coupling prediction model when compared with the results from the literature. Its excellent prediction performance lies in that the proposed coupling model can take full advantage of the systematic multitime historical data and catch the stochastic fluctuation tendency. This work also makes a significant contribution to the enrichment of grey prediction theory and the extension of its application span.

scholarly journals Long Short-Term Memory Network-Based Metaheuristic for Effective Electric Energy Consumption Prediction

2021 ◽  
Vol 11 (23) ◽  
pp. 11263
Author(s):  
Simran Kaur Hora ◽  
Rachana Poongodan ◽  
Rocío Pérez de Prado ◽  
Marcin Wozniak ◽  
Parameshachari Bidare Divakarachari
Keyword(s):  
Energy Management ◽  
Short Term Memory ◽  
Electric Energy ◽  
Short Term ◽  
Term Memory ◽  
Electric Energy Consumption ◽  
Long Short Term Memory ◽  
Lstm Network ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

The Electric Energy Consumption Prediction (EECP) is a complex and important process in an intelligent energy management system and its importance has been increasing rapidly due to technological developments and human population growth. A reliable and accurate model for EECP is considered a key factor for an appropriate energy management policy. In recent periods, many artificial intelligence-based models have been developed to perform different simulation functions, engineering techniques, and optimal energy forecasting in order to predict future energy demands on the basis of historical data. In this article, a new metaheuristic based on a Long Short-Term Memory (LSTM) network model is proposed for an effective EECP. After collecting data sequences from the Individual Household Electric Power Consumption (IHEPC) dataset and Appliances Load Prediction (AEP) dataset, data refinement is accomplished using min-max and standard transformation methods. Then, the LSTM network with Butterfly Optimization Algorithm (BOA) is developed for EECP. In this article, the BOA is used to select optimal hyperparametric values which precisely describe the EEC patterns and discover the time series dynamics in the energy domain. This extensive experiment conducted on the IHEPC and AEP datasets shows that the proposed model obtains a minimum error rate relative to the existing models.

Short term and Long term Building Electricity Consumption Prediction Using Extreme Gradient Boosting

2020 ◽  
Vol 13 ◽  
Author(s):  
Sakshi Tyagi ◽  
Pratima Singh
Keyword(s):  
Prediction Models ◽  
Electricity Consumption ◽  
Gradient Boosting ◽  
Support Vector ◽  
Short Term ◽  
Term Prediction ◽  
Extreme Gradient Boosting ◽  
Boosting Method ◽  
Consumption Prediction

Background: Electricity is considered as the basic essential unit in today’s high-tech world. The electricity demand has been increased very rapidly due to increased urbanization, smart buildings, and usage of smart devices to a large extent. Building a reliable and accurate electricity consumption prediction model becomes necessary with the increase in building energy. From recent studies, prediction models such as support vector regression (SVR), gradient boosting decision tree (GBDT), artificial neural network (ANN), random forest (RF), and extreme gradient boosting (XGBoost) have been compared for the prediction of electricity consumption and XGBoost is found to be most efficient thus leading to the motivation for the proposed research work. Objective: The objective of this research is to propose a model that performs future electricity consumption prediction for different time horizons: short term prediction and long term prediction using extreme gradient boosting method and reduce the prediction errors. In addition to this based on the prediction, best and worst predicted days are also recognized. Methods: The method used in this research is the extreme gradient boosting for future building electricity consumption prediction. The extreme gradient boosting method performs prediction for the short term and long term for different seasons. The model is trained on a household building in Paris. Results: The model is trained and tested on the dataset and it predicts accurately with the lowest errors compared to other machine learning techniques. The model predicts accurately with RMSE of 140.45 and MAE of 28 which is the least errors when compared to the baseline prediction models. Conclusion: A model that is robust to all the conditions should be built by enhancing the prediction mechanism such that the model should be dependent on less factors to make electricity consumption prediction.

scholarly journals Sequential Learning-Based Energy Consumption Prediction Model for Residential and Commercial Sectors

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 605
Author(s):  
Ijaz Ul Haq ◽  
Amin Ullah ◽  
Samee Ullah Khan ◽  
Noman Khan ◽  
Mi Young Lee ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Prediction Model ◽  
Short Term Memory ◽  
Electrical Energy ◽  
Sequential Learning ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

The use of electrical energy is directly proportional to the increase in global population, both concerning growing industrialization and rising residential demand. The need to achieve a balance between electrical energy production and consumption inspires researchers to develop forecasting models for optimal and economical energy use. Mostly, the residential and industrial sectors use metering sensors that only measure the consumed energy but are unable to manage electricity. In this paper, we present a comparative analysis of a variety of deep features with several sequential learning models to select the optimized hybrid architecture for energy consumption prediction. The best results are achieved using convolutional long short-term memory (ConvLSTM) integrated with bidirectional long short-term memory (BiLSTM). The ConvLSTM initially extracts features from the input data to produce encoded sequences that are decoded by BiLSTM and then proceeds with a final dense layer for energy consumption prediction. The overall framework consists of preprocessing raw data, extracting features, training the sequential model, and then evaluating it. The proposed energy consumption prediction model outperforms existing models over publicly available datasets, including Household and Korean commercial building datasets.

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