Prediction Of Electrical Energy Consumption Research Articles

Nonlinear Ensemble Deep Learning Model for Energy Consumption Prediction with Bayesian Optimization

Accurate prediction of electric energy consumption is crucial for efficient load dispatching, energy utilization, and grid operation. Traditional statistical and classical machine learning methods struggle with the nonlinear nature of energy consumption data, often leading to higher prediction errors. Additionally, deep learning models using a single approach face challenges such as convergence to local minima and poor generalization. This paper proposes a nonlinear ensemble deep learning model for residential energy consumption prediction, incorporating Bayesian optimization for hyperparameter tuning. The model combines Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and 1D Convolutional Neural Networks (1D-CNN), leveraging their powerful nonlinear feature learning capabilities. A k-means clustering approach is used to preprocess and reduce variability in the data, enhancing the ensemble model's performance. The ensemble model was tested on real energy consumption data from two districts in Addis Ababa, showing significant improvements in prediction accuracy with lower MAE, RMSE, and MAPE values compared to single models and un-clustered data. The integration of clustering and Bayesian optimization further enhanced model generalizability and minimized overfitting, demonstrating the effectiveness of a nonlinear approach in capturing complex energy consumption patterns.

Peak Electrical Energy Consumption Prediction by ARIMA, LSTM, GRU, ARIMA-LSTM and ARIMA-GRU Approaches

Forecasting peak electrical energy consumption is important because it allows utilities to properly plan for the production and distribution of electrical energy. This reduces operating costs and avoids power outages. In addition, it can help reduce environmental impact by allowing for more efficient power generation and reducing the need for additional fossil fuels during periods of high demand. In the current work, electric power consumption data from “Compagnie Electrique du Benin (CEB)” was used to deduce the peak electric power consumption at peak hours. The peak consumption of electric power was predicted using hybrid approaches based on traditional time series prediction methods (autoregressive integrated moving average (ARIMA)) and deep learning methods (long short-term memory (LSTM), gated recurrent unit (GRU)). The ARIMA approach was used to model the trend term, while deep learning approaches were employed to interpret the fluctuation term, and the outputs from these models were combined to provide the final result. The hybrid approach, ARIMA-LSTM, provided the best prediction performance with root mean square error (RMSE) of 7.35, while for the ARIMA-GRU hybrid approach, the RMSE was 9.60. Overall, the hybrid approaches outperformed the single approaches, such as GRU, LSTM, and ARIMA, which exhibited RMSE values of 18.11, 18.74, and 49.90, respectively.

Electrical energy consumption prediction using machine learning methods

Electrical energy consumption prediction using machine learning methods

Pump Feature Construction and Electrical Energy Consumption Prediction Based on Feature Engineering and LightGBM Algorithm

In recent years, research on improving the energy consumption ratio of pumping equipment through control algorithms has improved. However, the actual behavior of pump equipment and pump characteristic information do not always correspond, resulting in deviations between the calculated energy consumption operating point and the actual operating point. This eventually results in wasted power. To solve this problem, the data from circulating pumping equipment in a large pumping facility are analyzed, and the necessary characteristics of pumping equipment electrical energy consumption are analyzed through a subset of mechanism expansion feature engineering using the Pearson correlation coefficient algorithm. Based on this, a pump energy consumption prediction method based on LightGBM is constructed and compared with other algorithm models. To improve the generalization ability of the model, rules applicable to pump power energy consumption prediction are proposed, and the model features and processes are reduced. Based on the mechanistic model, 18 features related to electric energy consumption are selected, and 6 necessary features of pump electric energy consumption are screened by feature engineering. The experimental results show that the LightGBM regression algorithm has a significant prediction effect with R2=0.94. After the importance analysis, three features that are strongly related to pump energy consumption are finally screened out. According to the prediction results, the feature engineering dataset was selected and the pump electrical energy consumption was predicted based on the LightGBM algorithm, which can significantly reduce the problem of deviation in the prediction of the electrical energy consumption of pumping equipment.

Electric Vehicle Velocity and Energy Consumption Predictions Using Transformer and Markov-Chain Monte Carlo

Although electric vehicles (EVs) are becoming a promising alternative for ground transportation, the issue of limited battery energy hinders its market penetration. Accurate prediction of electrical energy consumption along a given route would significantly relieve drivers’ anxieties and build their confidence in EVs. A high-fidelity energy estimation relies on an accurate velocity forecast. To this end, this article proposes a novel hybrid deterministic-stochastic methodology that utilizes inputs encompassing the route information, the driver’s characteristics, and the traffic flow’s uncertainties to predict the EV’s future velocity profile and energy consumption. The method comprises two components: a deterministic machine-learning-based transformer network and a stochastic Markov-chain Monte Carlo (MCMC) algorithm. Real-world EV data collected in four routes of different lengths and features are used to evaluate the method. The results demonstrate the enhanced performance of the approach in both velocity prediction and energy consumption estimation, compared to two popular baseline algorithms.

Prediction of electrical energy consumption in Cameroon through econometric models

Electricity consumption is on the rise in developing countries. Most energy demand forecasting research studies aim to provide sufficient electricity forecast for accurate planning of investments in electricity generation and distribution. The main objective of this study is to develop efficient and realistic solutions for forecasting electricity consumption in Cameroon. This article proposes a hybrid model based on vector error correction models (VECM) and Holt-Winters exponential smoothening (HES), combines from an improved algorithm of the gradient descend in order to minimize the forecast error. The prediction results of this hybrid approach are compared to similar studies in the literature, to artificial intelligence models as well as to predictions from official published scientific work. Also, it is applied to forecast the future net electricity consumption of Cameroon until 2024. The results of the study indicate that the proposed model can generate more realistic and reliable forecasts. It can also be argued that it responds better to some unexpected reactions from the time series.

Performance evaluation of LSTM neural networks for consumption prediction

Energy consumption and energy efficiency are topics that have attracted the attention of researchers in recent years, in order to seek sustainable solutions for energy production and reduction of costs, aiming to provide a balance between development and protection of natural resources. One of the alternatives that have obtained satisfactory results is the use of technologies based on Internet of Things (IoT) and Deep Learning systems. Based on this, we assessed the performance of Long Short-Term Memory (LSTM) neural networks in time series electric energy consumption prediction, for a forecasting module of an IoT system. Three time series were used and we compared LSTM to the algorithms Extreme Boost Gradient and Random Forest. Computational results indicate that the LSTM model showed a tendency of better RMSE performance in the first data set, and statistically significant better results in other two data sets, according to the Kruskal-Wallis test (p < 0.0001 in both cases). Thus, the proposed model was implemented and validated from experiments, presenting accurate prediction for monitoring and estimating power consumption, being applicable to Energy Efficiency and decision making.

Long Short-Term Memory Network-Based Metaheuristic for Effective Electric Energy 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.

FORECASTING OF ELECTRICAL ENERGY CONSUMPTION OF HOUSEHOLDS IN A SMART GRID

This paper aims to develop a hybrid model for forecasting electrical energy consumption of households based on a Particle Swarm Optimization (PSO) algorithm associated with the Grey and Adaptive Neuro-Fuzzy Inference System (ANFIS). This paper proposes a new Grey-ANFIS-PSO model that is based on historical data from smart meters in order to estimate and improve the accuracy of forecasting electrical energy consumption. This accuracy will be characterized by coefficients such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). The PSO will allow to optimally design the Neuro-fuzzy forecasting. This method is implemented on Cameroon consumption data over the 24-years period in order to forecast energy consumption for the next years. Using this model, we were able to estimate that electricity consumption will be 1867 GWH in 2028 with 0.20158 RMSE and 0.62917% MAPE. The simulation results obtained show that implementation of this new optimized Neuro-fuzzy model on consumption data for a long period presents better results on prediction of electrical energy consumption compared to single artificial intelligence models of literature such as Support Vector Machine (SVM) and Artificial Neural Network (ANN).Keywords: Forecast model, PSO, ANFIS model, Grey model, electricity consumptionJEL Classifications: C22, C25, C32, C41, C45DOI: https://doi.org/10.32479/ijeep.11761

Prediction of Electric Energy Consumption using Recurrent Neural Networks

Prediction of Electric Energy Consumption using Recurrent Neural Networks

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.

Mid-term prediction of electrical energy consumption for crude oil pipelines using a hybrid algorithm of support vector machine and genetic algorithm

The mid-term electrical energy consumption forecasting for crude oil pipelines is helpful for making important decisions, such as energy consumption target setting, unit commitment, batch scheduling, and equipment monitoring with degraded performance. The electricity energy consumption forecasting during operation is complicated. Therefore, A hybrid prediction method combining genetic algorithm and support vector machine is proposed, which includes four parts: data preprocessing part, optimization part, forecasting part, and evaluation part. The stratified sampling method is adopted to divide the training set and the test set to avoid large deviation caused by sampling stochasticity of small samples. According to the nonlinear relationship between input variable and output variable mapped by SVM technology, genetic algorithm was proposed to optimize the hyperparameters of SVM. For the operation data of three crude oil pipelines in China, the different proportions of data sets are compared and analyzed, the ratio of training set to test set for Pipeline 1, Pipeline 2, and Pipeline 3 is 6:4, 7:3, 8:2, respectively. Comparing the evaluation indicators of GA-SVM with that of five state-of-the-art prediction methods, GA-SVM hybrid model has the best effect in improving the predictive accuracy, and the forecast results are in the best agreement with the actual data.

Prediction of electrical energy consumption based on machine learning technique

The forecast of electricity demand in recent years is becoming increasingly relevant because of market deregulation and the introduction of renewable resources. To meet the emerging challenges, advanced intelligent models are built to ensure precise power forecasts for multi-time horizons. The use of intelligent forecasting algorithms is a key feature of smart grids and an effective tool of resolving uncertainty for better cost and energy efficiency decisions like scheduling the generations, reliability and power optimization of the system, and economic smart grid operations. However, prediction accuracy in forecasting algorithms is highly demanded since many important activities of power operators like load dispatch depend upon the short-term forecast. This paper proposes a model for the estimation of the consumption of electricity in Agartala, Tripura in India, which can accurately predict the next 24 h of load with and estimation of load for 1 week to 1 month. A number of specific characteristics in the city have been analysed in order to extract variables that could affect the pattern of electricity consumption directly. In addition, the present paper shows the way to significantly improve the accuracy of the prediction through ensemble machine learning process. We demonstrated the performance of individual Random forest and XGBoost along with their ensemble. The RF and XGBoost ensemble obtained an accuracy with an improvement of 15–29%. The analyses or findings also provide interesting results in connection with energy consumption.

Analysis of modern approaches for the prediction of electric energy consumption

AbstarctA review of modern methods of forming a mathematical model of power systems and the development of an intelligent information system for monitoring electricity consumption. The main disadvantages and advantages of the existing modeling approaches , as well as their applicability to the energy systems of Ukraine and Kazakhstan,are identified. The main factors that affect the dynamics of energy consumption are identified. A list of the main tasks that need to be implemented in order to develop algorithms for predicting electricity demand for various objects, industries and levels has been developed.

Development of an IoT-Driven Building Environment for Prediction of Electric Energy Consumption

This article demonstrates the importance of integrating the Internet-of-Things (IoT) devices and technologies for efficient energy management in a building environment. An Elman recurrent neural network (RNN) model and an exponential model are developed for electric energy consumption prediction in an IoT-driven building environment. The models predict electric energy consumption by electric loads in the near future by: 1) recognizing the existence of a relationship between the net electric energy consumption of the building’s electric loads and the ambient temperature along with the occupancy state of the building and 2) employing the detected relationship to predict electric energy consumption using the forecasted temperature and the scheduled occupancy state. The building environment under consideration is the Real-Time Power and Intelligent Systems (RTPIS) laboratory integrated with intelligent monitoring and control capabilities using IoT devices and technologies. The electric loads under consideration include heating, ventilation, and air conditioning (HVAC) units and light panels. The developed Elman RNN and exponential models are also compared. These prediction capabilities are beneficial in overcoming variabilities in electric energy consumption by supplying electric energy as needed to meet the demands of the electric loads, thereby minimizing wasted electric energy, reducing carbon emissions, and generating cost savings.

Improving Electric Energy Consumption Prediction Using CNN and Bi-LSTM

The electric energy consumption prediction (EECP) is an essential and complex task in intelligent power management system. EECP plays a significant role in drawing up a national energy development policy. Therefore, this study proposes an Electric Energy Consumption Prediction model utilizing the combination of Convolutional Neural Network (CNN) and Bi-directional Long Short-Term Memory (Bi-LSTM) that is named EECP-CBL model to predict electric energy consumption. In this framework, two CNNs in the first module extract the important information from several variables in the individual household electric power consumption (IHEPC) dataset. Then, Bi-LSTM module with two Bi-LSTM layers uses the above information as well as the trends of time series in two directions including the forward and backward states to make predictions. The obtained values in the Bi-LSTM module will be passed to the last module that consists of two fully connected layers for finally predicting the electric energy consumption in the future. The experiments were conducted to compare the prediction performances of the proposed model and the state-of-the-art models for the IHEPC dataset with several variants. The experimental results indicate that EECP-CBL framework outperforms the state-of-the-art approaches in terms of several performance metrics for electric energy consumption prediction on several variations of IHEPC dataset in real-time, short-term, medium-term and long-term timespans.

A Comparative Study of Time Series Forecasting Methods for Short Term Electric Energy Consumption Prediction in Smart Buildings

Smart buildings are equipped with sensors that allow monitoring a range of building systems including heating and air conditioning, lighting and the general electric energy consumption. Thees data can then be stored and analyzed. The ability to use historical data regarding electric energy consumption could allow improving the energy efficiency of such buildings, as well as help to spot problems related to wasting of energy. This problem is even more important when considering that buildings are some of the largest consumers of energy. In this paper, we are interested in forecasting the energy consumption of smart buildings, and, to this aim, we propose a comparative study of different forecasting strategies that can be used to this aim. To do this, we used the data regarding the electric consumption registered by thirteen buildings located in a university campus in the south of Spain. The empirical comparison of the selected methods on the different data showed that some methods are more suitable than others for this kind of problem. In particular, we show that strategies based on Machine Learning approaches seem to be more suitable for this task.

Electric Energy Consumption Prediction by Deep Learning with State Explainable Autoencoder

As energy demand grows globally, the energy management system (EMS) is becoming increasingly important. Energy prediction is an essential component in the first step to create a management plan in EMS. Conventional energy prediction models focus on prediction performance, but in order to build an efficient system, it is necessary to predict energy demand according to various conditions. In this paper, we propose a method to predict energy demand in various situations using a deep learning model based on an autoencoder. This model consists of a projector that defines an appropriate state for a given situation and a predictor that forecasts energy demand from the defined state. The proposed model produces consumption predictions for 15, 30, 45, and 60 min with 60-min demand to date. In the experiments with household electric power consumption data for five years, this model not only has a better performance with a mean squared error of 0.384 than the conventional models, but also improves the capacity to explain the results of prediction by visualizing the state with t-SNE algorithm. Despite unsupervised representation learning, we confirm that the proposed model defines the state well and predicts the energy demand accordingly.

Electrical Energy Consumption Prediction is based on the Recurrent Neural Network

The results of forecasting of electric energy consumption based on recurrent neural network model. When developing the model, the following dominant factors were taken into account: data on energy consumption over the forecast period; meteorological factors (air temperature, cloudiness, amount of precipitation, wind speed, length of daylight, etc.); date (day, month); data of production calendars (information on the day of the week: weekday / weekend / holiday / shortened); specificity of the industry in the district under consideration (combining statistical information on major centers of federal districts). The factors were selected on the basis of test runs through the neural network of fixed configuration. The relevance of the study is explained by the practical importance of searching for the most accurate methods for predicting the main parameters of the Russian energy market, when a large error of the forecast subject to more expensive tariffs. The constructed recurrent neural network has yielded more accurate prediction results than the widely used mathematical prediction models based on regression dependencies. The obtained scientific result will help to reduce costs and increase the energy efficiency of the electro-energy subjects in the wholesale electric energy and capacity in Russia.

A hybrid teaching-learning artificial neural network for building electrical energy consumption prediction

Numerous data-driven models have been successfully adopted for electrical energy consumption forecasting at building and larger scales. When the data set for forecasting is multi-sourced, heterogeneous or inadequate, single data-driven model may lead to convergence problem or poor model accuracy. The combination of advanced evolutionary algorithms (EAs) and data-driven models is proved effective in terms of prediction accuracy and robustness improvements. However, some of them are very time consuming to converge. In this paper, a novel EA, i.e. teaching learning based optimization (TLBO), is proposed for short-term building energy usage prediction. To enhance its convergence speed and optimization accuracy, the basic TLBO algorithm is further modified in three aspects. The improved algorithm is combined with artificial neural networks (ANNs) and applied to hourly electrical energy prediction of two educational buildings located in USA and China respectively. Performance comparisons show that the proposed model has superior performances than previously reported GA-ANN and PSO-ANN methods in terms of convergence speed and predictive accuracy, and is suitable for online energy prediction in the future.