Forecasting Electricity Consumption Research Articles

Advancing building energy efficiency: A deep learning approach to early-stage prediction of residential electric consumption

Enhancing building energy efficiency underscores the critical need for innovative predictive models to mitigate environmental issues from high energy consumption, especially in residential areas with air-conditioning and heating ventilation systems. This study introduces the use of Long Short-Term Memory (LSTM) networks for early prediction of residential electric consumption, representing a significant innovation in the field. Unlike traditional Deep Neural Network (DNN) and Artificial Neural Network (ANN) models, Long Short-Term Memory networks efficiently process time-series data, predicting future energy usage with unmatched accuracy. The Long Short-Term Memory model exhibited superior training efficiency, requiring only 2.69 s for over 500 test cases, outperforming Deep Neural Network and Artificial Neural Network models, which took 5.26 and 3.88 s, respectively. Its predictive accuracy, evidenced by an R-squared value of 0.97, surpasses the 0.95 and 0.92 of Deep Neural Network and Artificial Neural Network models, respectively. This breakthrough enables accurate predictions of annual energy usage before construction starts and aids in identifying energy efficiency improvements early in the design process. Applying Long Short-Term Memory networks in this context marks a substantial advancement in predictive modeling for building energy consumption, equipping architects and engineers with a vital tool for designing energy-efficient buildings from the beginning. The innovation and quantitatively proven effectiveness of the Long Short-Term Memory model highlight its potential to revolutionize early-stage building design strategies, filling a crucial gap in the existing literature.

Advancing Electricity Consumption Forecasts in Arid Climates through Machine Learning and Statistical Approaches

This study investigated the impact of meteorological factors on electricity consumption in arid regions, characterized by extreme temperatures and high humidity. Statistical approaches such as multiple linear regression (MLR) and multiplicative time series (MTS), alongside the advanced machine learning method Extreme Gradient Boosting (XGBoost) were utilized to analyze historical consumption data. The models developed were rigorously evaluated using established measures such as the Coefficient of Determination (R2), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The performance of the models was highly accurate, with regression-type models consistently achieving an R2 greater than 0.9. Additionally, other metrics such as RMSE and MAPE demonstrated exceptionally low values relative to the overall data scale, reinforcing the models’ precision and reliability. The analysis not only highlights the significant meteorological drivers of electricity consumption but also assesses the models’ effectiveness in managing seasonal and irregular variations. These findings offer crucial insights for improving energy management and promoting sustainability in similar climatic regions.

Development of an ARIMAX model for forecasting airport electricity consumption in Accra-Ghana: The role of weather and air passenger traffic

The rapid expansion of the aviation industry has significantly increased energy consumption within airport terminals. This study introduces an advanced ARIMAX model to forecast electricity consumption at Kotoka International Airport (KIA) in Ghana by incorporating weather conditions and air passenger numbers. By analysing data from 2019 to 2022, including lockdown periods, the model demonstrates superior predictive accuracy, evidenced by an RMSE of 55,914 kWh, a MAPE of 4.22 %, and an R-squared value of 0.899. Key determinants of energy consumption identified include air passengers and temperature, highlighting their substantial impact on energy use. The ARIMAX model's accuracy underscores its potential for enhancing energy management practices at airports. By integrating variables such as air passenger traffic and weather conditions, the model refines energy demand predictions, supporting more efficient energy use and operational planning. The ARIMAX model proves to be a valuable tool for forecasting energy consumption at KIA, aiding in energy management and sustainability efforts.

Заходи з енергозбереження в метрополітені (на прикладі біметалевих контактних рейок)

The purpose of this study is to develop methods and tools for long-term forecasting of electricity consumption based on an improved integrated method for predicting electricity demand, taking into account the peculiarities of electric use of the Kyiv Metro and its impact on the city's electricity consumption. The scientific novelty of the results obtained is the consideration of technological potential with measures for electric saving, pouring into Level II and Level III in the energy consumption model, which takes into account both the overall potential of electric saving (from structural and technological shifts) in the country as a whole, and the features at the regional level (the city of Kyiv) in the types of economic activity in the provision of transport services (Metro). During the work, material was collected on measures for electricity saving in the metro and forecasts for the development of this type of service provision (passenger transportation). To achieve sufficient energy consumption, it is important to introduce energy-efficient technologies that will reduce electricity costs without losing productivity and passenger service quality. Bimetallic contact rails are more efficient in terms of transmitting electric current and reducing energy losses. This innovation opens up new opportunities for the sustainable development of urban transport and contributes to the creation of more efficient and convenient conditions for city residents. Measures are provided to protect the structures and equipment of the section that is being designed and built, the metro line from electric corrosion. Calculation of electricity saving potentials at the regional level (Kyiv) made it possible to more accurately take into account the volume of implementation of energy saving measures, since such programs are developed at the regional level. These estimates are used to determine the technological potential of energy saving. The technological potential of electricity saving will reach the level of 600 million kWh by 2040, while maintaining the pace of economic development in Kyiv. Keywords: Metro, electric saving, electric saving potential, forecasting, energy efficiency, electricity.

Seasonal Electrical Load Forecasting Using Machine Learning Techniques and Meteorological Variables

Accurate forecasting of seasonal power consumption is crucial for effective grid management, especially with increasing energy demand and renewable energy integration. Weather patterns significantly influence energy usage, making load prediction a challenging task. This study employs machine learning algorithms, including Random Forest (RF), Artificial Neural Networks (ANN), and Decision Tree (DT) models, to forecast electricity consumption using meteorological variables such as solar irradiance, humidity, and ambient temperature. The impact of weather elements on load prediction accuracy across different seasons is explored using seasonal forecasting techniques. The results demonstrate the superior performance of ANN and RF models in forecasting summer and winter loads compared to the rainy season. This discrepancy is attributed to the abundance of data for the summer and winter seasons, and the ability of the models to capture complex patterns within the data for these particular seasons. The study highlights the potential of machine learning techniques, particularly ANN and RF, in conjunction with meteorological data analysis, for enhancing the accuracy of seasonal electrical load forecasting. This can contribute to more effective power grid management and support the transition towards a more sustainable energy landscape. The findings underscore the importance of data quality, quantity, and appropriate model selection for different seasonal conditions.

A Hybrid Approach for Hierarchical Forecasting of Industrial Electricity Consumption in Brazil

The Brazilian industrial sector is the largest electricity consumer in the power system. Energy planning in this sector is important mainly due to its economic, social, and environmental impact. In this context, electricity consumption analysis and projections are highly relevant for the decision-making of the industrial sectorand organizations operating in the energy system. The electricity consumption data from the Brazilian industrial sector can be organized into a hierarchical structure composed of each geographic region (South, Southeast, Midwest, Northeast, and North) and their respective states. This work proposes a hybrid approach that incorporates the projections obtained by the exponential smoothing and Box–Jenkins models to obtain the hierarchical forecasting of electricity consumption in the Brazilian industrial sector. The proposed approach was compared with the bottom-up, top-down, and optimal combination approaches, which are widely used for time series hierarchical forecasting. The performance of the models was evaluated using the mean absolute percentage error (MAPE) and root mean squared error (RMSE) precision measures. The results indicate that the proposed hybrid approach can contribute to the projection and analysis of industrial sector electricity consumption in Brazil.

Forecasting Turkish electricity consumption: A critical analysis of single and hybrid models

Forecasting of electricity consumption is a critical issue, due to its importance in the planning of the energy trading countries. Several new techniques such as hybrid models are used as well as classical single models to estimate electricity consumption. This study aims to get the best electricity consumption model of Türkiye. For this, the forecasting performances of single and hybrid electricity consumption models, SARIMA is the time series model, ANNs and MLPs are machine learning single models and SARIMA-ANNs and SARIMA-MLPs are hybrid models of machine learning, are compared. This study employs new hybrid models and examines whether the multiplicative model of Wang et al. or the combined model of Khashei and Bijari is superior to than Zhang's hybrid model commonly used as the ARIMA-hybrid model with well known flaws. The results show that hybrid models are more accurate than single time series/machine learning models when forecasting Turkish electricity consumption. Moreover, The Khashei and Bijari hybrid model outperformed the other models and it was determined as the best model for forecasting Türkiye's electricity consumption.

A two-stage electricity consumption forecasting method integrated hybrid algorithms and multiple factors

Electricity consumption forecasting provides a reference basis for electricity scheduling, it has become a research hotspot in the field of electric power. The fluctuation of electricity consumption is influenced by multiple factors, which has strong randomness and are strongly disturbed by noise. Single algorithms cannot ensure the stability and accuracy of electricity consumption forecasting. To address this problem, a two-stage framework for electricity consumption forecasting is proposed in this paper. In the first stage, multi-forecasting algorithms are considered to eliminate noise and forecast the electricity consumption trend. A hybrid model is constructed, which consists of three parts: Ensemble Empirical Mode Decomposition (EEMD), Gated Recurrent Unit (GRU) and Gradient Boosting Regression Tree (GBRT). The EEMD method is employed to decompose the electricity consumption signal into different frequency components. GRU and GBRT are adopted to forecast high-frequency and low-frequency components respectively. In the second stage, multidimensional meteorological factors are considered to further improve the accuracy of the electricity consumption forecasting. A structure for feature extraction is constructed to reduce the dimension of meteorological factors. The experimental results indicate that the two-stage framework model outperforms mainstream measurement models by an average reduction of 35.7% and 43.2% in MAE and RMSE.

Utilizing Machine Learning Techniques for Power Estimation in Residential Electricity Consumption

Electricity consumption analysis and estimation play a vital role in various contexts, ranging from business planning to household financial management. In an era where electrical energy is a primary source of human activity, understanding power usage patterns and estimating them becomes crucial. This research enables an understanding of energy consumption from individual to national scales and supports efficient energy policy planning. Power estimation provides insights into the expenditures that consumers or companies will incur, enabling effective financial planning. Research and development of accurate predictive models using machine learning techniques are essential to providing useful information for the energy industry and the general public. This study uses machine learning to compare the performance of different power prediction models. The decision tree (DT) method, with an MAE of 0.3613, an MSE of 0.4184, and an RMSE of 0.6469, gives the best results. The results show that the model can provide accurate predictions of electricity consumption, potentially becoming a reliable tool in power management and financial management. Its contribution is crucial in the energy industry and daily life, providing insights needed to enhance energy efficiency and budget management. As a result, this research provides relevant and beneficial solutions to energy and financial management for society and industry. Keywords: Electricity consumption analysis, Estimation, Machine learning, Power management, financial management

Investigating the Impact of Data Normalization Methods on Predicting Electricity Consumption in a Building Using different Artificial Neural Network Models.

The study investigates the impact of data normalization on the prediction of electricity consumption in buildings using four multilayer Artificial Neural Networks (ANN) algorithms: Long Short-Term Memory Networks (LSTM), Levenberg-Marquardt Back-propagation (LMBP), Recurrent Neural Networks (RNN), and General Regression Neural Network (GRNN). Four data normalization approaches, Min-Max Scaling, Mean, Z-score, and Gaussian function were assessed on experimental datasets. The LSTM algorithm, when combined with Min-Max normalization, showed the most favorable predictive capabilities, with a low Coefficient of Variation of the Root Mean Square Error (CVRMSE) of 10.3 and Normalized Mean Bias Error (NMBE) of 0.6. The remaining three normalization approaches showed satisfactory concordance with empirical data, but with slight disparities in precision. The LMBP model, when using Z-score normalization, had favorable performance in forecasting electricity consumption, but the discrepancies across the models were not significant. The Recurrent Neural Network (RNN) model, when used with Gaussian normalization, exhibited the most favorable performance, with the lowest Coefficient of Variation of Root Mean Square Error (CVRMSE) at 11.8 and Normalized Mean Biased Error (NMBE) at 0.6. The Generalized Regression Neural Network (GRNN) model, trained on unprocessed data, exhibited superior performance, with the lowest Coefficient of Variation of Root Mean Square Error (CVRMSE) at 19.2 and NMBE at 1.0. In conclusion, the study highlights the significant influence of data normalization on the predictive capabilities of various ANN models, suggesting that careful use of data normalization techniques can significantly improve the accuracy of electricity consumption forecasting in buildings.

Multi-strategy Hybrid Coati Optimizer: A Case Study of Prediction of Average Daily Electricity Consumption in China

Multi-strategy Hybrid Coati Optimizer: A Case Study of Prediction of Average Daily Electricity Consumption in China

Improving electricity demand forecasting accuracy: a novel grey-genetic programming approach using GMC(1,N) and residual sign estimation

PurposeThis paper addresses the challenges associated with forecasting electricity consumption using limited data without making prior assumptions on normality. The study aims to enhance the predictive performance of grey models by proposing a novel grey multivariate convolution model incorporating residual modification and residual genetic programming sign estimation.Design/methodology/approachThe research begins by constructing a novel grey multivariate convolution model and demonstrates the utilization of genetic programming to enhance prediction accuracy by exploiting the signs of forecast residuals. Various statistical criteria are employed to assess the predictive performance of the proposed model. The validation process involves applying the model to real datasets spanning from 2001 to 2019 for forecasting annual electricity consumption in Cameroon.FindingsThe novel hybrid model outperforms both grey and non-grey models in forecasting annual electricity consumption. The model's performance is evaluated using MAE, MSD, RMSE, and R2, yielding values of 0.014, 101.01, 10.05, and 99% respectively. Results from validation cases and real-world scenarios demonstrate the feasibility and effectiveness of the proposed model. The combination of genetic programming and grey convolution model offers a significant improvement over competing models. Notably, the dynamic adaptability of genetic programming enhances the model's accuracy by mimicking expert systems' knowledge and decision-making, allowing for the identification of subtle changes in electricity demand patterns.Originality/valueThis paper introduces a novel grey multivariate convolution model that incorporates residual modification and genetic programming sign estimation. The application of genetic programming to enhance prediction accuracy by leveraging forecast residuals represents a unique approach. The study showcases the superiority of the proposed model over existing grey and non-grey models, emphasizing its adaptability and expert-like ability to learn and refine forecasting rules dynamically. The potential extension of the model to other forecasting fields is also highlighted, indicating its versatility and applicability beyond electricity consumption prediction in Cameroon.

A federated and transfer learning based approach for households load forecasting

The escalating global population and increased smart-devices demand have made electricity load estimation a significant challenge for relevant authorities. Even with advancements in Artificial Intelligence, pinpointing both immediate and long-term electricity requirements remain a challenging and complex task. Predicting electricity usage at the household level can offer insights into this trend, aiding in the development of efficient load management strategies. However, creating individual predictive models for every household is a resource-intensive task. The current research proposes a streamlined approach that merges clustering analysis with federated and transfer learning to craft household-level predictive models. Firstly, clustering analysis is employed to discern electricity usage patterns, forming cluster-specific datasets. Subsequently, a federated learning model is introduced to each identified cluster, ensuring that the foundational model is finely calibrated to the unique consumption patterns inherent to each group. To evaluate the effectiveness of our approach, we employed Root Mean Square Error and Mean Absolute Error as evaluation metrics. During the evaluation phase, the transfer learning approach is employed, fine-tuning our root model on 70% of the data and testing its performance on the remaining 30%. The robustness of the proposed methodology is validated using a comprehensive dataset detailing electricity usage across Australian households. When bench-marked against prevailing methodologies, our proposed model consistently demonstrates either closer or superior predictive accuracy, emphasizing its potential as a formidable tool in real-time electricity consumption forecasting.

Deep Time Series Forecasting Models: A Comprehensive Survey

Deep learning, a crucial technique for achieving artificial intelligence (AI), has been successfully applied in many fields. The gradual application of the latest architectures of deep learning in the field of time series forecasting (TSF), such as Transformers, has shown excellent performance and results compared to traditional statistical methods. These applications are widely present in academia and in our daily lives, covering many areas including forecasting electricity consumption in power systems, meteorological rainfall, traffic flow, quantitative trading, risk control in finance, sales operations and price predictions for commercial companies, and pandemic prediction in the medical field. Deep learning-based TSF tasks stand out as one of the most valuable AI scenarios for research, playing an important role in explaining complex real-world phenomena. However, deep learning models still face challenges: they need to deal with the challenge of large-scale data in the information age, achieve longer forecasting ranges, reduce excessively high computational complexity, etc. Therefore, novel methods and more effective solutions are essential. In this paper, we review the latest developments in deep learning for TSF. We begin by introducing the recent development trends in the field of TSF and then propose a new taxonomy from the perspective of deep neural network models, comprehensively covering articles published over the past five years. We also organize commonly used experimental evaluation metrics and datasets. Finally, we point out current issues with the existing solutions and suggest promising future directions in the field of deep learning combined with TSF. This paper is the most comprehensive review related to TSF in recent years and will provide a detailed index for researchers in this field and those who are just starting out.

Applicability of data-driven methods in modeling electricity demand-climate nexus: A tale of Singapore and Hong Kong

Predicting electricity demand at city level is vital for stakeholders. There are various data-driven methods in electricity consumption prediction, but their applicability to forecast monthly electricity demand has yet to be systematically explored. This study examines several widely used data-driven methods, namely multiple linear regression (MLR), machine learning (ML) method including support vector machine (SVM) and random forest (RF), and deep learning (DL) method including long short-term memory network (LSTM) and LSTM-gated recurrent unit (GRU), for long-term energy prediction using climatic and historical electricity datasets in Singapore (2005–2019) and Hong Kong (1975–2019). In Singapore, ML outperforms other methods in terms of statistical criteria and time series plots, and SVM provides the best accuracy with mean absolute percentile error (MAPE) of 2.55 %, while MLR exhibits the worst accuracy with MAPE of 3.1 %. In Hong Kong, DL surpasses ML in terms of statistical criteria, time series stability, and generalization ability. MLR achieves the best overall prediction accuracy with R2 up to 0.95 but shows poor ability for predicting peak and low electricity consumption, while LSTM exhibits the smallest bias in these months. RF shows strong overfitting issues in both cities. Overall, SVM and LSTM are recommended for small and large datasets, respectively.

Day-Ahead Electricity Consumption Prediction of Individual Household–Capturing Peak Consumption Pattern

Day-Ahead Electricity Consumption Prediction of Individual Household–Capturing Peak Consumption Pattern

Enhancing hourly electricity forecasting using fuzzy cognitive maps with sample entropy

Accurate electricity consumption forecasting is the crucial to ensure the stable operation of the power system and optimize the smart grid dispatch. However, existing methods may not effectively capture complex potential features and change patterns, and are susceptible to noise, leading to deficiencies such as suboptimal electricity consumption forecasting accuracy, poor stability, and weak generalization capabilities. To fill this gap, a high order fuzzy cognitive map (FCM) forecasting method based on sample entropy and minimax concave penalty (MCP) regularization is proposed. Firstly, two mode decomposition methods are innovatively fused by utilizing sample entropy to extract the complex features of electricity consumption data, then a high order FCM model with powerful system state prediction capability and interpretable knowledge representation is constructed, and finally a FCM learning method based on MCP regularization is proposed. The forecasting method has the merits of low forecasting error, high stability and strong generalization capabilities. Comprehensive experimental results on multiple electricity consumption datasets show that the proposed method achieves optimal performance compared to 10 state-of-the-art baseline methods, demonstrating effectiveness in the task of electricity consumption forecasting, which contributes to more efficient energy management and cost savings, and provides a reliable tool for smart grid planning and management.

An advanced framework for net electricity consumption prediction: Incorporating novel machine learning models and optimization algorithms

In recent years, the escalating demand for electric energy has underscored the need for robust prediction models capable of accurately anticipating consumption patterns. The imperative lies in enabling utilities and policymakers to optimize resource allocation, strategically plan infrastructure development, and ensure the stability and efficiency of the power grid. This study undertakes a comprehensive comparative analysis of machine learning techniques employed in predicting net electricity consumption in Turkey. The primary goal is to augment the accuracy and performance of electricity load forecasting, thereby contributing to effective energy management and fostering sustainable development within the power sector. Two machine learning models, including CatBoost and Extreme Gradient Boosting (XGBoost), are strategically integrated with optimization algorithms such as Sparrow Search Algorithm (SSA), Phasor Particle Swarm Optimization (PPSO), and Hybrid Grey Wolf Optimization (GWO). The core analysis centers on evaluating the performance of these integrated models based on key accuracy metrics and runtime efficiency. Notably, the results underscore that the XGBoost-SSA model emerges as the superior performer, exhibiting heightened accuracy and superior performance in predicting electricity consumption. This model showcases the highest coefficient of determination (R2) value and demonstrates lower errors during the testing phase, thereby presenting a promising and effective approach for electricity consumption prediction in the specific context of Turkey.

Prediction of industrial electricity consumption based on grey cluster weighted Markov model

Accurate prediction of industrial electricity consumption is not only beneficial to maintaining the steady development of the economy but also to conserving energy. To improve the prediction accuracy of industrial electricity consumption, a grey cluster weighted Markov model is proposed. It is applied to predict the industrial electricity consumption in four different regions in China. The prediction results are compared with the traditional discrete grey prediction model, which shows that the present model is more effective in these aspects of prediction accuracy, stability and extensibility. The research can provide theoretical references for the “West-East electricity transmission project” in China.

Internet of Vehicles (IoV) Based Framework for electricity Demand Forecasting in V2G

The integration of smart grids with Advanced Metering Infrastructure (AMI) has bridged the realms of the Internet of Vehicles (IoV) and Electric Vehicles (EVs), yet challenges persist in managing EV Battery Range and charging infrastructure effectively. This paper presents an innovative IoV-based framework tailored for EVs, with a specific focus on forecasting electricity consumption in a Vehicle-to-Grid (V2G) scenario. By exploring the hurdles surrounding electric vehicle usage, the research lays the foundation for Electric Vehicles (EVs). The proposed IoV model optimizes IoT sensor utilization through a fog layer and employs a Back Propagation (BP) neural network for battery State of Charge (SoC) estimation, integrating Principal Component Analysis (PCA) for data dimensionality reduction. Leveraging substantial computing capabilities, the cloud layer predicts Electricity Consumption Data (ECD) associated with EVs in V2G scenarios. Performance evaluation metrics like Akaike Information Criteria (AIC), Mean Error (ME), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE) are assessed across state-of-the-art forecasting algorithms. Incorporating EV potential into the optimal model reveals a significant 10% reduction in electricity demand. This research advances IoV-based frameworks, offering insights to enhance EV efficiency within the broader energy infrastructure.