Consumption Forecasting Research Articles

Application and evaluation of the evolutionary algorithms combined with conventional neural network to determine the building energy consumption of the residential sector

Residential uses a significant amount of energy; hence, encouraging sustainability and lessening environmental effects requires minimizing energy consumption in this sector. This study focuses on applying and evaluating evolutionary algorithms combined with conventional neural networks to predict building energy consumption in the residential sector. The primary objectives were to assess the performance of three evolutionary algorithms – Heap-Based Optimizer (HBO), Multiverse Optimizer (MVO), and Whale Optimization Algorithm (WOA) – in comparison to each other and to determine their effectiveness in predicting energy consumption. Each algorithm was integrated into the neural network framework to optimize the prediction model. Training and testing datasets were employed to evaluate the performance of the models. Two key statistical indices, Root Mean Square Error (RMSE) and R-squared (R2), were utilized to assess the accuracy of the predictions. The results of the evaluation demonstrated varying performances among the three evolutionary algorithms. MVO achieved the highest scores for both RMSE (48.55082 in training and 68.44517 in testing) and R2 (0.99184 in training and 0.98236 in testing) on both training and testing datasets, indicating superior predictive accuracy compared to HBO and WOA. These findings underscore the importance of algorithm selection in optimizing predictive models for energy consumption forecasting. Further research may explore hybrid approaches or parameter tuning to enhance the performance of evolutionary algorithms in this domain. Overall, this study contributes to advancing energy forecasting techniques, with potential implications for energy management and conservation efforts in the residential sector.

Analysis and forecast of college student canteen consumption based on TL-LSTM

Analysis and forecast of college student canteen consumption based on TL-LSTM

Innovative hybrid prediction method integrating wavelet threshold decomposition and entropy-based model selection strategy for building energy consumption prediction

Building energy prediction plays a pivotal role in the building energy management system, and an efficient prediction method is crucial to ensure the effective operation of building systems. Existing research enhances prediction accuracy by combining decomposition algorithms with prediction methods to separate non-stationary information from the original sequence. However, the decomposition process and the subsequent prediction of multiple decomposed subsequences result in significant time overhead, making it unsuitable for scenarios with high temporal requirements, such as short-term energy consumption forecasting. Additionally, a single prediction model struggles to adapt to the diverse features of decomposed subsequences, creating prediction accuracy and efficiency bottlenecks. Addressing these challenges, this study proposes a hybrid prediction method that combines wavelet threshold decomposition (WTD) with an entropy-based model selection strategy. The method initially utilizes the efficient WTD algorithm to decompose the original energy consumption data into three fixed subsequences. Subsequently, the permutation entropy for each subsequence is computed, and based on the comprehensive performance of four prediction models (ANN, LSTM, CNN-LSTM, and TCN) on sequences with different entropy values, the optimal prediction model is selected. Finally, the prediction results of the subsequences are aggregated to obtain the ultimate prediction outcome. Experimental results demonstrate that the proposed method significantly outperforms baseline methods in both prediction speed and accuracy across five real building energy consumption datasets with distinct operational modes.

Machine learning for electric energy consumption forecasting: Application to the Paraguayan system

Abstract In this paper we address the problem of short-term electric energy prediction using a time series forecasting approach applied to data generated by a Paraguayan electricity distribution provider. The dataset used in this work contains data collected over a three-year period. This is the first time that these data have been used; therefore, a preprocessing phase of the data was also performed. In particular, we propose a comparative study of various machine learning and statistical strategies with the objective of predicting the electric energy consumption for a given prediction horizon, in our case seven days, using historical data. In this paper we have tested the effectiveness of the techniques with different historical window sizes. Specifically, we considered two ensemble strategies, a neural network, a deep learning technique and linear regression. Moreover, in this study, we tested whether the inclusion of meteorological data can help achieve better predictions. In particular, we considered data regarding temperature, humidity, wind speed and atmospheric pressure registered during the three-year period of data collection. The results show that, in general, the deep learning approach obtains the best results and that such results are obtained when meteorological data are also considered. Moreover, when meteorological data is used, a smaller historical window size is required to obtain precise predictions.

A Novel Deep Learning-Based Data Analysis Model for Solar Photovoltaic Power Generation and Electrical Consumption Forecasting in the Smart Power Grid

With the installation of solar panels around the world and the permanent fluctuation of climatic factors, it is, therefore, important to provide the necessary energy in the electrical network in order to satisfy the electrical demand at all times for smart grid applications. This study first presents a comprehensive and comparative review of existing deep learning methods used for smart grid applications such as solar photovoltaic (PV) generation forecasting and power consumption forecasting. In this work, electrical consumption forecasting is long term and will consider smart meter data and socioeconomic and demographic data. Photovoltaic power generation forecasting is short term by considering climatic data such as solar irradiance, temperature, and humidity. Moreover, we have proposed a novel hybrid deep learning method based on multilayer perceptron (MLP), long short-term memory (LSTM), and genetic algorithm (GA). We then simulated all the deep learning methods on a climate and electricity consumption dataset for the city of Douala. Electrical consumption data are collected from smart meters installed at consumers in Douala. Climate data are collected at the climate management center in the city of Douala. The results obtained show the outperformance of the proposed optimized method based on deep learning in the both electrical consumption and PV power generation forecasting and its superiority compared to basic methods of deep learning such as support vector machine (SVM), MLP, recurrent neural network (RNN), and random forest algorithm (RFA).

Long Term Forecasting of Peak Demand and Annual Electricity Consumption of the West African Power Pool Interconnected Network by 2032

The uneven distribution of primary sources of electric power generation in Economic Community of West African States (ECOWAS) compelled the heads of states to create the West African Power Pool (WAPP). The vision of this system is to set up a common electrical energy market to satisfy the balance between supply and demand at an affordable price using the interconnected network. Forecasting maximum power demand and energy consumption is essential for planning and the coordination of new power plant and transmission lines building. This work consists of predicting maximum power demand and total energy that must transit through the WAPP interconnected network by the year 2032. We compare the performances of three time series models namely the Long Short-Term Memory (LSTM), Auto-Regressive Integrated Moving Average (ARIMA) and Fb Facebook Prophet. Electric power and energy data used for training the systems comes from the WAPP authorties. The results show that, for monthly peaks, the Facebook (Fb) Prophet model is the best, with a MAPE (mean absolute error percentage) of 3.1% and a low RMSE (root mean square error) of 1.225 GW. For energy prediction, ARIMA performances are the best compared to others with (RMSE 1.20 TWh, MAPE 1.00%). Thus, the forecast for total annual energy consumption and annual peak demand will be, respectively, 96.85TWh and 13.6 GW in 2032.

A Trapezoid Attention Mechanism for Power Generation and Consumption Forecasting

A Trapezoid Attention Mechanism for Power Generation and Consumption Forecasting

Transformer-Based Forecasting for Sustainable Energy Consumption Toward Improving Socioeconomic Living: AI-Enabled Energy Consumption Forecasting

Transformer-Based Forecasting for Sustainable Energy Consumption Toward Improving Socioeconomic Living: AI-Enabled Energy Consumption Forecasting

Time series forecasting of electricity consumption using hybrid model of recurrent neural networks and genetic algorithms

The forceful energy efficiency to manage the demand is essential to meet development goals. Palestine has suffered from an electricity deficit, whereas the city of Tulkarm suffers from a chronic one. The dataset was collected from Tulkarm city in Palestine; this city is considered one of the cities that suffers the most from frequent power outages. It's difficult to determine the most powerful Artificial intelligence (AI) approaches that can accurately forecast electricity consumption. This paper presents a hybrid model that combines Recurrence Neural Networks (RNNs) and Genetic Algorithms (GAs) [RNN-GAs] to forecast electricity consumption and optimize demand. In the proposed model the K-means clustering technique produces specific initial population seeding and optimization crossover operators to enhance the efficiency and find the optimal solution. The results showed that the proposed Nonlinear Autoregressive with External (Exogenous) (NARX) (NARX-GAs) with the K-means clustering technique outperforms the hybrid model NARX-GAs. The NARX-GAs-K Mean Clustering recorded an RMSE value of 0.08759, which performs a good balance with the lowest RMSE, especially in long-term forecasting, and also outperforms the other hybrid forecasting models that depend on RNN-GAs. Finally, the forecasting results of the hybrid NARX-GAs-K Mean Clustering can predict accurately the energy consumption in a city, which leads to the use of the model in similar cities to forecast and manage the demand for electricity consumption.

Load Balance Forecasting Based on Hybrid Deep Neural Network

Load forecasting is the foundation of utility design, and it is a fundamental business problem in the utility industry. Load forecasting, mainly referring to forecasting electricity demand and energy, is being used throughout all segments of the electric power industry, including generation, transmission, distribution, and retail. In this paper, a long short-term memory network with a hybrid approach is improved with a dense algorithm and proposed for electricity load forecasting. A long short-term memory network is designed to effectively exhibit the dynamic behavior of load time series. The proposed model is tested for Panama study including historical data and weather variables. The prediction accuracy is validated by performance metrics, and the best of the metrics are attained when mean absolute error is 5.262, mean absolute percentage error 0.0000376, and root mean square error 18.243. The experimental results show a high prediction rate for load balance forecasting of electric power consumption.

A discrete time-varying grey Fourier model with fractional order terms for electricity consumption forecast

Accurate electricity consumption forecast plays an important role in managing and operating a country’s power system and is a challenging task due to the data exhibiting the mixture characteristics of trend, periodicity and randomness. To accurately describe the dynamic changes of seasonal fluctuations of electricity consumption, a novel discrete time-varying grey Fourier model with fractional order terms is proposed by coupling Fourier functions with fractional order time-varying terms, as the grey action, to fit the amplitude variations hidden in seasonal pattern and improve the fitting and forecasting accuracy. Moreover, the mechanism and properties of the proposed model are discussed. Then the optimal fractional order is determined by the Dingo Optimization Algorithm (DOA) and the optimal Fourier order is selected by the hold-out method to enhance the robustness of the model. Subsequently, the results of designed experiments based on Monte-Carlo method testify the validity of the Fourier order selection method and the proposed model. Finally, this model is applied to predict the monthly residential electricity consumption of China from 2015 to 2022. The results indicate that the proposed model captures the dynamic amplitude variations of the time series and has better prediction performances than other benchmark grey prediction models and non-grey prediction models.

Deep learning-based forecasting of electricity consumption

Building energy management systems (BEMS) are integrated computerized systems that track and manage the energy use of many pieces of building-related machinery and equipment, including lighting, power systems, and HVAC systems. Modern buildings must have BEMSs in order to reduce energy usage while maintaining comfort. Not only for energy-saving purposes, BEMS is essential in enhancing the quality of the energy supply, which helps to gain a better understanding of how energy is used and the building's energy usage. When the dynamics of a building's energy usage are known, it is possible to determine which changes are most likely to reduce consumption. Numerous connected devices, operating modes, energy usage, and environmental factors can all be monitored and controlled in real-time using BEMS. Changing operating times and setting points to maximize comfort and efficiency is made simple by this. In this paper, we have primarily addressed the two significant issues of model optimization and electricity consumption forecasts. Future forecasting has been done using the LSTM based time series approach. We generated data on the amount of electricity consumed by a hospital facility and tested our suggested methodologies on actual data. The findings gained demonstrated that the strategies were successful with both types of data. On actual data, the trend in electricity consumption can be accurately predicted. Several model optimizers enhanced the suggested methods' performance as well. Our objective function gain accuracy result of 95%.

Power consumption prediction for electric vehicle charging stations and forecasting income

Electric vehicles (EVs) are the future of the automobile industry, as they produce zero emissions and address environmental and health concerns caused by traditional fuel-poared vehicles. As more people shift towards EVs, the demand for power consumption forecasting is increasing to manage the charging stations effectively. Predicting power consumption can help optimize operations, prevent grid overloading, and power outages, and assist companies in estimating the number of charging stations required to meet demand. The paper uses three time series models to predict the electricity demand for charging stations, and the SARIMA (Seasonal Auto Regressive Integrated Moving Average) model outperforms the ARMA (Auto Regressive Moving Average) and ARIMA (Auto Regressive Integrated Moving Average) models, with the least RMSE (Root Mean Squared Error), MAE (Mean Absolute Error) and MAPE (Mean Absolute Percentage Error) scores in forecasting power demand and revenue. The data used for validation consists of charging activities over a four-year period from public charging outlets in Colorado, six months of charging data from ChargeMOD's public charging terminals in Kerala, India. Power usage is also forecasted based on wheels of vehicles, and finally, a plan subscription data from the same source is utilized to anticipate income, that helps companies develop pricing strategies to maximize profits while remaining competitive. Utility firms and charging networks may use accurate power consumption forecasts for a variety of purposes, such as power scheduling and determining the expected energy requirements for charging stations. Ultimately, precise power consumption forecasting can assist in the effective planning and design of EV charging infrastructure. The main aim of this study is to create a good time series model which can estimate the electric vehicle charging stations usage of power and verify if the firm has a good income along with some accuracy measures. The results show that SARIMA model plays a vital role in providing us with accurate information. According to the data and study here, four wheelers use more power than two and three wheelers. Also, DC charging facility uses more electricity than AC charging stations. These results can be used to determine the cost to operate the EVs and its subscriptions.

Urban Water Consumption: A Systematic Literature Review

The study and analysis of urban water consumption habits in different regions contribute to the development of strategies aimed at secure water reduction and distribution. Within this context, knowledge of global water availability and the analysis of factors that influence consumption in different regions in distinct situations become extremely important. Several studies have been carried out in a number of countries and describe different approaches. The objective of this article is to learn about the strategies used in water consumption forecast and analysis. Most of the studies analysed seek to understand the factors influencing consumption in different building types. When it comes to residential buildings, the number of residents and the influence of economic issues on water consumption have an important role in this matter. In this context, pieces of research present the use of awareness campaigns as a strategy towards water use reduction. As a contribution, this article presents a systemic view of the pieces of research conducted and their contribution to forecasting water consumption in different regions. In conclusion, one observes the importance of analysing the factors influencing water consumption in different regions and scenarios, such as during the COVID-19 pandemic. This article can help managers and researchers understand the main factors that influence water consumption and how this consumption takes place in different regions.

Fueling the Future: A Comprehensive Analysis and Forecast of Fuel Consumption Trends in U.S. Electricity Generation

The U.S. Energy Information Administration (EIA) provides crucial data on monthly and annual fuel consumption for electricity generation. These data cover significant fuels, such as coal, petroleum liquids, petroleum coke, and natural gas. Fuel consumption patterns are highly dynamic and influenced by diverse factors. Understanding these fluctuations is essential for effective energy planning and decision making. This study outlines a comprehensive analysis of fuel consumption trends in electricity generation. Utilizing advanced statistical methods, including time series analysis and autocorrelation, our objective is to uncover intricate patterns and dependencies within the data. This paper aims to forecast fuel consumption trends for electricity generation using data from 2015 to 2022. Several time series forecasting models, including all four benchmark methods (Mean, Naïve, Drift, and seasonal Naïve), Seasonal and Trend Decomposition using Loess (STL), exponential smoothing (ETS), and the Autoregressive Integrated Moving Average (ARIMA) method, have been applied. The best-performing models are determined based on Root Mean Squared Error (RMSE) values. For natural gas (NG) consumption, the ETS model achieves the lowest RMSE of 20,687.46. STL demonstrates the best performance for coal consumption with an RMSE of 5936.203. The seasonal Naïve (SNaïve) model outperforms the others for petroleum coke forecasting, yielding an RMSE of 99.49. Surprisingly, the Mean method has the lowest RMSE of 287.34 for petroleum liquids, but the ARIMA model is reliable for its ability to capture complex patterns. Residual plots are analyzed to assess the models’ performance against statistical parameters. Accurate fuel consumption forecasting is very important for effective energy planning and policymaking. The findings from this study will help policymakers strategically allocate resources, plan infrastructure development, and support economic growth.

Sensitivity analysis and comparative assessment of novel hybridized boosting method for forecasting the power consumption

This research focuses on the crucial task of accurately forecasting electricity consumption, a key concern in modern societies where electricity is essential for industries, healthcare, and transportation. The study explores the complex factors influencing electricity usage, aiming to improve the accuracy of consumption forecasts. Recognizing the limitations of current forecasting techniques, which often fail to account for the detailed interplay of variables affecting consumption, it introduces innovative methods to address these challenges. By employing sensitivity analysis and advanced hybrid boosting algorithms, such as LightGBM and CatBoost optimized with Arithmetic Optimization and Fruit Fly Optimization Algorithms, the research analyzes time-series data on electricity use, weather conditions, and usage timing across three power distribution networks. The sensitivity analysis reveals the significant impact of customer behavior and temperature on consumption patterns. The predictive models, especially the optimized LightGBM, demonstrate remarkable accuracy, with rates of 0.9996 and 0.9968 for training and testing datasets, respectively. These results highlight the effectiveness of the proposed methodologies in providing accurate and reliable forecasts of electricity demand, offering valuable insights for the energy sector to manage resources more efficiently and meet consumption needs effectively.

DBSCAN-based energy users clustering for performance enhancement of deep learning model

Background: Due to rapid progress in the fields of artificial intelligence, machine learning and deep learning, the power grids are transforming into Smart Grids (SG) which are versatile, reliable, intelligent and stable. The power consumption of the energy users is varying throughout the day as well as in different days of the week. Power consumption forecasting is of vital importance for the sustainable management and operation of SG. Methodology: In this work, the aim is to apply clustering for dividing a smart residential community into several group of similar profile energy user, which will be effective for developing and training representative deep neural network (DNN) models for power load forecasting of users in respective groups. The DNN models is composed of convolutional neural network (CNN) followed by LSTM layers for feature extraction and sequence learning respectively. The DNN For experimentation, the Smart Grid Smart City (SGSC) project database is used and its energy users are grouped into various clusters. Results: The residential community is divided into four groups of customers based on the chosen criterion where Group 1, 2, 3 and 4 contains 14 percent, 22 percent, 19 percent and 45 percent users respectively. Almost half of the population (45 percent) of the considered residential community exhibits less than 23 outliers in their electricity consumption patterns. The rest of the population is divided into three groups, where specialized deep learning models developed and trained for respective groups are able to achieve higher forecasting accuracy. The results of our proposed approach will assist researchers and utility companies by requiring fewer specialized deep-learning models for accurate forecasting of users who belong to various groups of similar-profile energy consumption.

Short-Term Energy Consumption Prediction of Large Public Buildings Combined with Data Feature Engineering and Bilstm-Attention

Accurate building energy consumption prediction is a crucial condition for the sustainable development of building energy management systems. However, the highly nonlinear nature of data and complex influencing factors in the energy consumption of large public buildings often pose challenges in improving prediction accuracy. In this study, we propose a combined prediction model that combines signal decomposition, feature screening, and deep learning. First, we employ the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to decompose energy consumption data. Next, we propose the Maximum Mutual Information Coefficient (MIC)-Fast Correlation Based Filter (FCBF) combined feature screening method for feature selection on the decomposed components. Finally, the selected input features and corresponding components are fed into the Bi-directional Long Short-Term Memory Attention Mechanism (BiLSTMAM) model for prediction, and the aggregated results yield the energy consumption forecast. The proposed approach is validated using energy consumption data from a large public building in Shaanxi Province, China. Compared with the other five comparison methods, the RMSE reduction of the CEEMDAN-MIC-FCBF-BiLSTMAM model proposed in this study ranged from 57.23% to 82.49%. Experimental results demonstrate that the combination of CEEMDAN, MIC-FCBF, and BiLSTMAM modeling markedly improves the accuracy of energy consumption predictions in buildings, offering a potent method for optimizing energy management and promoting sustainability in large-scale facilities.

Cognitive Modelling and Forecasting of Electricity Consumption

Purpose of reseach. Development of a forecast model of energy consumption and assessment of factors influencing its consumption. The obtained forecast estimates of energy consumption will improve the quality and efficiency of management decisions at all levels of administrative management.Methods. The article presents an analytical review of the existing methods of cognitive modelling and forecasting of electric power consumption, the description of the software implementation of the information-computing system that allows to make a forecast of electric power consumption by the population of the administrative-territorial formation. The approach to the description of factors of electric power consumption by both population and various branches of national economy, as well as organisations engaged in rendering various services has been proposed. Special software has been developed, which allows to obtain model results of electric power consumption in an automated mode, to carry out factor analysis of power consumption. The experimental verification of the work of the programme of cognitive modelling and forecasting of electric power consumption by the population of Lgovsky district of Kursk region is given. The developed software also makes it possible to evaluate the adequacy of the obtained results and promptly adjust the model parameters.Results. As a result of the research a fuzzy cognitive map of energy consumption for a municipal entity was developed. The concepts of the subject area describing the influence of various groups of factors on the level of electric energy consumption were identified. Forecast estimates of electricity consumption were obtained, which were based on the data for the retrospective period. Adequacy indicators based on the calculation of statistical criteria are determined for the obtained estimates.Conclusion. The results of the study have shown that the combination of cognitive and statistical methods allows to achieve an adequate solution when solving the problem of energy consumption forecasting.

Anomaly detection in time-series data using evolutionary neural architecture search with non-differentiable functions

Deep neural networks have become the benchmark in diverse fields such as energy consumption forecasting, speech recognition, and anomaly detection, owing to their ability to efficiently process and analyse data. However, they face challenges in managing the complexity and variability in time series data, often leading to increased model complexity and prolonged search duration during parameter tuning. This paper proposes a novel anomaly detection approach through evolutionary neural architecture search (AD-ENAS), which is specifically designed for time series data. The proposed approach focuses on the search for the optimal and minimal neural network architecture. The AD-ENAS method consists of two main phases: architecture evolution and weight adjustment. The architecture evolution phase highlights the importance of neural network architecture by evaluating the fitness of each network agent using shared weight values. Subsequently, the convolutional matrix adaptation technique is used in the next phase for optimal weight adjustment of the neural network. The proposed AD-ENAS method operates without relying on differentiable functions, thus expanding the scope of neural network design beyond traditional backpropagation-based approaches. Various non-differentiable loss functions are explored to facilitate effective architecture search and weight adjustment. Comparative experiments are conducted with five baseline anomaly detection methods on three well-known datasets from reputable sources such as NASA SMAP, NASA MSL and Yahoo S5-A1. The results demonstrate that the AD-ENAS approach effectively evolves neural network architectures, outperforming baseline methods with F1 scores across the three datasets (MSL: 0.942, SMAP: 0.961, Yahoo S5-A1: 0.988) with non-differentiable loss functions, showcasing its efficacy in detecting anomalies in time series data.