Consumption Forecasting Research Articles

Predicting the impact of climate change on building energy consumption by using data-driven approaches

Buildings are complex thermodynamic entities that account for a large proportion of energy consumption. This work explores the application of data-driven models in order to forecast the building energy consumption over long time horizons. Long Short-Term Memory and Random Forest are used to forecast hourly heating and cooling energy consumption in the Urban Sciences Building, which is located in the city of Newcastle upon Tyne, UK. A synthetic time-series dataset is constructed using (a) a validated EnergyPlus model and (b) operational data hosted by Newcastle Urban Observatory. The energy consumption forecast is made using future climate data that represent a high emission future scenario during a typical year, that is 2030, and 2080. The experimental results suggest that, on average over the next six decades, there will be a 45% reduction in annual heating load, accompanied by a 680% increase in annual cooling load. These early findings indicate a notable shift in building thermal performance, a rise in overall building energy demand under the more drastic future weather scenarios, and a sharper emphasis on designing building envelopes and energy systems to include decarbonised cooling solutions.

A real-time prediction framework for energy consumption of electric buses using integrated Machine learning algorithms

An accurate prediction of energy consumption in electric buses (EBs) can effectively reduce driving range anxiety and facilitate bus scheduling. Existing studies have not provided real-time predictions based on distance traveled using integrated machine learning methods. This study proposes a framework for predicting EB energy consumption, which is primarily divided into energy consumption estimation, kinematic feature prediction, and energy consumption prediction. The framework begins by fusing high-resolution real-world EB data with weather and road information, from which five types of influencing factors are extracted for different driving distances. An eXtreme Gradient Boosting (XGBoost) model is developed to evaluate feature importance and estimate the energy consumption rate (ECR). The SHapley Additive explanation (SHAP) method is then used to analyze the factors affecting the ECR. To predict important kinematic characteristics, spatial and temporal characteristics are captured using Long Short-Term Memory (LSTM) and a fully connected neural network. Finally, the predicted kinematic characteristics and the XGBoost model are combined to enable real-time prediction of the ECR. The results indicate that estimation and prediction accuracies gradually improve with increased driving distance. The mean absolute error of average ECR decreases from 43.9 % for 100 m to 7.5 % for 16 km. Temperature, bus stop density, and peak periods emerge as the most significant external factors after 8 km. This framework shows an improvement of over 10 % in most scenarios compared with other models in the literature, enabling individual forecasts of energy consumption currently in transit and aiding in the calculation of remaining battery-supported distance.

Online energy consumption forecast for battery electric buses using a learning-free algebraic method

Accurately predicting the energy consumption plays a vital role in battery electric buses (BEBs) route planning and deployment. Based on the algebraic derivative estimation, we present a novel method to forecast the energy consumption in real time. In contrast to the mainstream machine-learning-based methods, the proposed method does not require access to the historical energy consumption data. It eliminates the time-consuming and computationally expensive offline training. Consequently, its prediction performance is not constrained by the quantity and quality of the training data. Moreover, the method can swiftly adapt to new situations not included in the previous driving cycles, which makes it especially suitable for emerging transport modes, e.g., on-demand transit services. In addition, its online execution only involves algebraic calculations, yielding superior calculation efficiency. Using real-world data, we comprehensively compare the performance of the proposed learning-free algebraic method with multiple representative machine-learning-based methods. Finally, the advantages and limitations of the proposed method are discussed in detail.

Energy consumption forecasting for oil and coal in China based on hybrid deep learning

The consumption forecasting of oil and coal can help governments optimize and adjust energy strategies to ensure energy security in China. However, such forecasting is extremely challenging because it is influenced by many complex and uncertain factors. To fill this gap, we propose a hybrid deep learning approach for consumption forecasting of oil and coal in China. It consists of three parts, i.e., feature engineering, model building, and model integration. First, feature engineering is to distinguish the different correlations between targeted indicators and various features. Second, model building is to build five typical deep learning models with different characteristics to forecast targeted indicators. Third, model integration is to ensemble the built five models with a tailored, self-adaptive weighting strategy. As such, our approach enjoys all the merits of the five deep learning models (they have different learning structures and temporal constraints to diversify them for ensembling), making it able to comprehensively capture all the characteristics of different indicators to achieve accurate forecasting. To evaluate the proposed approach, we collected the real 880 pieces of data with 39 factors regarding the energy consumption of China ranging from 1999 to 2021. By conducting extensive experiments on the collected datasets, we have identified the optimal features for four targeted indicators (i.e., import of oil, production of oil, import of coal, and production of coal), respectively. Besides, we have demonstrated that our approach is significantly more accurate than the state-of-the-art forecasting competitors.

A Novel Approach to Faster Convergence and Improved Accuracy in Deep Learning-Based Electrical Energy Consumption Forecast Models for Large Consumer Groups

A Novel Approach to Faster Convergence and Improved Accuracy in Deep Learning-Based Electrical Energy Consumption Forecast Models for Large Consumer Groups

Daily allocation of energy consumption forecasting of a power distribution company using optimized least squares support vector machine

Daily allocation of energy consumption forecasting of a power distribution company using optimized least squares support vector machine

MACLSTM: A Weather Attributes Enabled Recurrent Approach to Appliance-Level Energy Consumption Forecasting

MACLSTM: A Weather Attributes Enabled Recurrent Approach to Appliance-Level Energy Consumption Forecasting

Smart Grid Load Forecasting Models Using Recurrent Neural Network and Long Short-Term Memory

This paper presents two methods for managing electrical energy consumption and demand, with the objective of developing reliable and accurate forecasting models for smart electrical network energy consumption and optimization. The first method utilizes a recurrent neural network (RNN), while the second employs long short-term memory (LSTM) techniques. This approach builds upon previous studies that have explored the use of machine learning models for energy forecasting, but often with limited performance or the inability to capture long-term dependencies in the data. The study utilizes the Global Energy Forecast 2012 database - for the period from 2004 to 2008, with a focus on electricity consumption - to validate the performance of the proposed models. The R-squared (R2) score is used as the primary evaluation metric, with the LSTM model achieving a remarkable 90% R2 score, outperforming the RNN model's 80% R2 score. This is a significant improvement over previous studies, which have typically reported R2 scores in the range of 70-80% for energy forecasting models. Furthermore, the LSTM model demonstrates superior error rate performance, with a Mean Squared Error (MSE) of 4.345%, compared to the RNN model's 16.644% MSE. This highlights the ability of LSTM models to capture long-term dependencies in the data, which is crucial for accurate energy consumption forecasting, a limitation often observed in traditional RNN-based approaches. The findings of this study highlight the superior performance of the LSTM-based approach in accurately predicting energy consumption in smart grids, a crucial aspect for optimizing energy management and distribution. This contribution is particularly significant, as it showcases the advantages of LSTM models over traditional RNN techniques in the context of energy forecasting, providing valuable insights for researchers and practitioners in the field of smart grid optimization, where accurate forecasting is essential for efficient energy management and distribution.

Medium-Term Forecasting of Power Consumption by ANN Considering Meteo-Factors

The main task of forecasting power consumption is to analyze objective factors affecting load changes and compute expected schedules of consumption to support energy companies organize the purchase and production of the necessary electricity in the proper volume. However, modern practical requirements for the accuracy of predictive calculations lead to the fact that previously developed methods do not always provide the necessary accuracy of computations. This is because several factors affect the level of power consumption and the accuracy of forecasting, including meteorological, where the growing use of both wind and photovoltaic energy worldwide leads the power systems to become highly dependent on the weather conditions. This study presents the influence of meteorological factors on the medium-term forecast of electricity consumption based on the power system of the Gorno-Badakhshan Autonomous Oblast in the Republic of Tajikistan, where in winter, due to an increase in demand for electricity and a drop in the water level in rivers, to ensure balance, requires additional commissioning of diesel power plants. Thus, it leads to an increase in the costs of electricity production in the region and environmental degradation. To increase the forecasting effectiveness, the authors propose an approach based on the clustering of meteorological conditions, where each cluster has its regression model. The Principal Component Analysis was proposed for aggregating meteorological factors.

Forecasting electricity consumption using the Principal Component Analysis method

Relevance. The need for accurate forecasting of electricity consumption to improve efficiency and reduce costs at industrial enterprises, which leads to increased competitiveness of goods manufactured by the enterprise. Traditional forecasting methods often do not take into account complex interactions between various factors affecting energy consumption and do not provide the necessary forecast accuracy. The principal component analysis method offers a promising prospect – reducing the volume of processed data (dimensionality) without significant loss of information, which simplifies forecast models while maintaining their accuracy. Aim. To develop an accurate and efficient model for forecasting electricity consumption at industrial enterprises using the principal component analysis method. This model is aimed at eliminating the limitations of traditional forecasting approaches by reducing data dimensionality and increasing the accuracy of predictions, which ultimately improves the efficiency of electricity consumption and reduces financial costs, including those due to forecasting errors. Methods. The principal component analysis method, which allows us to reduce the volume of processed data (dimensionality) by transforming a large set of correlated variables into a smaller set of uncorrelated principal components. The study included the following stages: data import and factor analysis, correlation matrix construction, analysis of selected and accumulated variances for each factor, factor loading matrix construction, dimension reduction, development of a mathematical model using linear regression, and forecast installation and validation. Results. The application of the principal component analysis method allowed us to create a model for forecasting electricity consumption. Its application showed that the first principal component explains 69.65% of the total variance, the second component – ​​17.28%, i. e. together they explain almost 87% of the variance. The developed model provides good agreement between the actual and forecast values ​​of electricity consumption in several time intervals with an average error level within the range of +3 to –5%. This indicates the suitability of the model for forecasting electricity consumption, although some discrepancies indicate the need for its further improvement.

Rules-Based Energy Management System for an EV Charging Station Nanogrid: A Stochastic Analysis

The article presents the development of a Rules-Based Energy Management System for a nanogrid that serves an electric vehicle charging station. This nanogrid is composed of photovoltaic generation, a wind turbine, a battery energy storage system, and a fast electric vehicle charger. The objective is to prioritize the use of renewable energy sources, reducing costs and promoting energy efficiency. The methodology includes forecasting models based on an Artificial Neural Network for photovoltaic generation, a parametric estimation for wind generation, and a Monte Carlo simulation to predict the energy consumption of electric vehicles. The developed algorithm makes decisions every 15 min, considering variables such as energy tariff, battery state of charge, renewable generation forecast, and energy consumption forecast. The results showed that the system adequately balances energy generation, consumption, and storage, even under forecasting uncertainties. The use of the Monte Carlo simulation was crucial for evaluating the financial impacts of forecast errors, enabling robust decision-making. This energy management system proved to be effective and sustainable for nanogrids dedicated to electric vehicle charging, with the potential to reduce operational costs and increase energy reliability and the use of renewable energy sources.

A Multivariate Intuitionistic Fuzzy Grey Model for Forecasting Electricity Consumption

Background: Whether in the short, medium or long term, forecasting electricity consumption has always been an essential study area. In the literature, many methods are used for future forecasting and are being improved daily to achieve better results. Objective: The main objective of this study is to make the most accurate long-term electricity consumption forecast, which is the basis for optimal future planning in the energy sector. Electric consumption forecasting is performed regionally since planning at the regional level is essential for more precise planning. Methods: There may be different variables that affect electricity consumption. This study ex-tends the multivariate grey model for electricity consumption prediction to intuitionistic trian-gular fuzzy numbers for nine regions. In the grey model, population, export, and gross domes-tic product variables were used as independent variables, and future predictions for these vari-ables were obtained through the univariate intuitionistic triangular fuzzy grey model. Results: The results of the proposed method are compared with those of the classical univari-ate grey model, univariate intuitionistic triangular fuzzy grey model, and classical multivariate grey model. The results show that the error values of the proposed method are lower. Conclusion: The study contributes to the development of the grey model. More accurate pre-diction results are obtained with the proposed method compared to similar methods

A hybrid AI model for forecasting electricity volume to optimize water supply company efficiency

Most water supply companies consume a large amount of electricity to ensure technological processes of water purification and distribution. However, even though Vodokanals are a large consumer of electricity, the forecasting of electricity consumption is still not given priority. An accurate forecast of the amount of electricity consumption will allow optimization of the distribution of consumption, reducing the values of peak consumption and in general reducing the electricity costs. In this study, deep learning methods are proposed to predict the daily electrical load during a month. Where the performance of deep learning artificial neural networks and hybrid neural networks are compared, this study, based on the comparison of various deep learning methods, proposes to increase the effectiveness of the application of artificial neural networks by their hybridization, to forecast the daily electrical load in the monthly period. We combined the gray wolf optimizer (GWO) and the group data processing method (GMDH) to predict the optimal amount of electrical load in water utilities. Keywords—extrapolation; forecasting; observation data; plotting position formulas; uncertainty.

Predicting Electricity Consumption using an Innovative Deep Energy Predictor Model for Enhanced Accuracy and Efficiency

Accurate forecasting of electricity consumption is crucial for efficient energy management and planning. Traditional models often struggle to capture the complex, nonlinear relationships inherent in energy consumption data, leading to less reliable predictions. In the quest to improve the accuracy and efficiency of electricity consumption predictions, we introduce the Deep Energy Predictor Model (DEPM). This innovative hybrid model combines the strengths of XGBoost for classification and Deep Neural Networks (DNN) for deep learning (DL) capabilities. The model leverages advanced feature extraction techniques using Cascaded ResNet, ensuring the capture of intricate patterns within the data. Implemented in Python, the DEPM highlights an impressive accuracy of 98%, with a precision of 0.97, recall of 0.99, and an F1-score of 0.98, setting a new standard for predictive models in the energy sector. This study elaborates on the model architecture, implementation details, and the evaluation metrics that underscore the model's superior performance. Our results demonstrate the potential of DEPM to significantly enhance the reliability of electricity consumption forecasts, providing a robust tool for energy management and planning.

Leveraging Feature Sets and Machine Learning for Enhanced Energy Load Prediction: A Comparative Analysis

Accurate cooling consumption forecasts are crucial for optimizing energy management, storage, and overall efficiency in interconnected HVAC systems. Weather conditions, building characteristics, and operational parameters significantly impact prediction accuracy. Since meteorological conditions highly influence cooling demand, leveraging external air data and user metrics offers a promising approach to estimate a building's hourly cooling energy usage. This study addresses the gap in existing research by comprehensively analyzing the performance of various machine learning algorithms, including ensemble learning and deep learning models, to improve prediction accuracy. By leveraging weather conditions, building characteristics, and operational parameters, we aim to predict cooling consumption across multiple systems (Cooling Ceiling, Ventilation, Free Cooling, and Total Cooling). Data from four weather stations, encompassing diverse features relevant to the European Central Bank (ECB) building's cooling consumption in Frankfurt, were employed. Our methodology includes the use of K-Nearest Neighbor, Decision Tree, Support Vector Regression, Linear Regression, Random Forest, Gradient Boosting, XGBoost, Adaboost, Long-Short-Term Memory, and Gated Recurrent Unit. Models. The results consistently demonstrate the superiority of the Random Forest model across different weather stations and feature sets. This model achieved a Mean Squared Error of approximately 0.002-0.003, Mean Absolute Error of around 0.031-0.034, and Root Mean Squared Error of about 0.052-0.069. These findings contribute to improved building cooling load management, promoting insights into optimal energy utilization and sustainable building practices. Doi: 10.28991/ESJ-2024-08-06-01 Full Text: PDF

Methodological Approaches to Electricity Consumption Forecast on Electric Cars

Methodological Approaches to Electricity Consumption Forecast on Electric Cars

Multi-output discrete grey model tailored for electricity consumption forecast

To address the limitation of many conventional grey forecasting methods that prioritize temporal dynamics analysis while often overlooking the essential spatial characteristics, we developed a multi-output discrete grey model tailored for forecasting electricity consumption in China's Yangtze River Delta, incorporating spatial effects. Specifically, we design a dynamic spatial interaction matrix to precisely capture the spatial correlations among neighboring areas, and integrate multiple sets of fixed-effect parameters to adeptly address spatial heterogeneity. Additionally, we implement regularization technique to prevent overfitting and utilize the Bayesian Optimization algorithm for hyper-parameter adjustment, considerably enhancing the model's stability of parameter estimation and its resilience to noise. Finally, a comprehensive case study, benchmarked against seven other models, highlights our model's outstanding multi-step predictive accuracy and robustness against input data uncertainty, exceeding the performance of existing methods.

A Novel Multi-Objective Hybrid Evolutionary-Based Approach for Tuning Machine Learning Models in Short-Term Power Consumption Forecasting

Accurately forecasting power consumption is crucial important for efficient energy management. Machine learning (ML) models are often employed for this purpose. However, tuning their hyperparameters is a complex and time-consuming task. The article presents a novel multi-objective (MO) hybrid evolutionary-based approach, GA-SHADE-MO, for tuning ML models aimed at solving the complex problem of forecasting power consumption. The proposed algorithm simultaneously optimizes both hyperparameters and feature sets across six different ML models, ensuring enhanced accuracy and efficiency. The study focuses on predicting household power consumption at hourly and daily levels. The hybrid MO evolutionary algorithm integrates elements of genetic algorithms and self-adapted differential evolution. By incorporating MO optimization, GA-SHADE-MO balances the trade-offs between model complexity (the number of used features) and prediction accuracy, ensuring robust performance across various forecasting scenarios. Experimental numerical results show the superiority of the proposed method compared to traditional tuning techniques, and random search, showcasing significant improvements in predictive accuracy and computational efficiency. The findings suggest that the proposed GA-SHADE-MO approach offers a powerful tool for optimizing ML models in the context of energy consumption forecasting, with potential applications in other domains requiring precise predictive modeling. The study contributes to the advancement of ML optimization techniques, providing a framework that can be adapted and extended for various predictive analytics tasks.

Water Use Attribution Analysis and Prediction Based on the VIKOR Method and Grey Neural Network Model: A Case Study of Zhangye City

Water consumption forecasting is a critical aspect of the increasingly strained water resources and sustainable water management processes. It is essential to explore the current status of water use patterns and future development directions in Zhangye City. In this study, 17 factors affecting water consumption in Zhangye City were selected to analyze changes in water consumption and to predict values from 2003 to 2022, utilizing the entropy weight–VIKOR model and the grey neural network model. The results indicate that agricultural water consumption and annual rainfall are the factors with the largest weights among the social and natural attribute indicators, respectively, significantly influencing water consumption in Zhangye City. As the proportions of water consumption for forestry, animal husbandry, fishery, livestock, urban public use, and ecological environment increase, while agricultural water consumption continues to decline, the overall water consumption trend in Zhangye City from 2003 to 2022 shows a positive trajectory. Each water consumption factor is tending toward greater balance, and the relationship between water supply and distribution is improving. The multi-year average relative error of the water consumption predictions for Zhangye City from 2003 to 2022 using the grey neural network model was 4.28%. Furthermore, the relative error values for annual predictions ranged from 0.60% to 5.00%, achieving an accuracy rate of 80.00%. This indicates a strong predictive performance. Ultimately, the model was used to predict a water consumption of 20.18 × 108 m3 in Zhangye City in 2027. The model can serve as a theoretical reference for short-term water consumption forecasting and for establishing a basin water resource allocation system in Zhangye City.

Estimating Expected Asset Returns with the Present Value Model of Consumption and Fed Forecasts

Abstract This paper utilizes Greenbook forecasts of consumption and income to estimate expected asset returns through a present value model of consumption. The study finds that, despite the valuable information contained in Greenbook forecasts, the expected asset returns obtained from this approach do not provide meaningful insights into future asset returns. This contrasts with previous literature suggesting predictability using the present-value model.