Consumption Forecasting Research Articles

Optimal active and reactive energy management for a smart microgrid system under the Moroccan grid pricing code

This article presents an innovative active and reactive energy management system (AR-EMS) specifically designed for residential buildings in Morocco, seamlessly integrated with a Smart Microgrid (SMG) and the Electrical Power Grid (EPG) supplier. The considered SMG incorporates a Photovoltaic system (PV) and an Energy Storage System (ESS). In Morocco, the EPG utilizes a Range Tariff System (RTS), where the tariff structure depends on the total energy consumed at the end of each month. Hence, the proposed AR-EMS is designed to computes optimal active and reactive power setpoints of each source at 15-minute intervals for the next 30 days, based on forecasts of energy consumption and PV power production. The AR-EMS consists of two distinct layers: the Long-Term Optimization Layer (LTOL) and the Short-Term Optimization Layer (STOL). The LTOL focuses on optimizing active and reactive power flow over an extended time horizon of 30 days, relying on forecasted data. In contrast, the STOL involves short-term forecasting and energy optimization over the next 24-hour horizon, addressing the LTOL forecasting uncertainties. The proposed strategy emphasizes a multi-objective problem aiming at minimizing both active and reactive energy bill, ESS degradation cost, Peak-to-Average Ratio (PAR) and carbon emissions. The considered problem is solved using a metaheuristic algorithm, specifically the Lexicographic approach within the Particle Swarm Optimization (PSO) method. To validate the effectiveness of the proposed approach, two comparative analyses are conducted. The first involves the impact of time horizons on the energy cost and the management performances within the Moroccan context. The second comparison, highlights the benefits and shortfalls of using simultaneous active and reactive power management, comparing it with a system exclusively managing active power (A-EMS). Both compared strategies are evaluated using the Moroccan tariff RTS and real world data. The comparative analysis highlights the advantages of the proposed AR-EMS, demonstrating benefits in terms of active and reactive energy bill reduction by 58.9% and 83.2%, respectively, and showcases a 49.3% reduction in carbon emissions while improving the EPG power factor by 6% compared to A-EMS, In conclusion, the optimization results are encouraging the Moroccan government to further develop the installation of SMGs.

Potato Consumption Forecasting Based on a Hybrid Stacked Deep Learning Model

AbstractPotato consumption forecasting is crucial for several stakeholders in the food market. Due to the market flexibility, the farmers can manipulate the volumes planted for a given type of produce to reduce costs and improve revenue. Consequently, it means that establishing optimal inventories or inventory levels is possible and critical in that sense for the sellers to avoid either inadequate inventory or excessive inventories that may lead to wastage. In addition, governments can predict future food deficits and put measures in place to guarantee that they have a steady supply of food some of the time, especially in regions that involve the use of potatoes. Increased potato-eating anticipation has advantages for the sellers and buyers of the potatoes. The experiments of this study employed various machine learning and deep learning (DL) models that comprise stacked long short-term memory (Stacked LSTM), convolutional neural network (CNN), random forest (RF), support vector regressor (SVR), K-nearest neighbour regressor (KNN), bagging regressor (BR), and dummy regressor (DR). During the study, it was discovered that the Stacked LSTM model had superior performance compared to the other models. The Stacked LSTM model achieved a mean squared error (MSE) of 0.0081, a mean absolute error (MAE) of 0.0801, a median absolute error (MedAE) of 0.0755, and a coefficient of determination (R2) value of 98.90%. These results demonstrate that our algorithms can reliably forecast global potato consumption until the year 2030.

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.

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.

Kazakhstan dairy market: guidelines for optimization

Milk plays an important role in human life as obligatory and necessary food product, containing the most valuable and easily digestible proteins, fats, carbohydrates, and minerals. The goal is to show the current state of dairy industry in Kazakhstan, factors influencing its development. The economic parameters of production of milk and dairy products, dynamics of production volumes, and the number of cattle by type of management have been studied. The methods are based on the study of domestic and analytical reviews, scientific publications, and statistical information. Results – there is a shortage of raw materials for processing enterprises, their low quality, and a large number of small producers. The authors state that homemade milk from private farms does not meet technological standards, which, in turn, is the reason for poorquality industrial processing of raw materials. Domestic producers satisfy the need only for liquid processed milk and cream (94.9%). Conclusions – indicators that have restraining effect on development of dairy industry in the republic are considered, the need for technological reequipment, formation of cluster structures, and state support for Kazakh producers is pointed out. Compliance with raw milk quality standards is only possible when using the latest methods for obtaining and primary processing of raw milk. To achieve an effect, subsidies for technical equipment are required, especially for small-scale farms (milking and refrigeration machines, disinfectants, express control analyzers, etc.). Forecasts for consumption of dairy products assume the ability to determine in the future the volumes and structure of market demand for this type of product. The fundamental factors are the level of income, trends in changes in retail prices, degree of market saturation, national and historical traditions.

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 large-scale evaluation of machine learning algorithms in mid-term water demand forecasting

ABSTRACT Water utilities currently face challenges in accurately measuring user-end consumption as they have to visit every conventional meter that is installed in their service area. This study explores the use of accessible machine learning models to forecast water demand at two crucial temporal scales, addressing practical needs such as customer billing. To draw meaningful conclusions, these models were tested and trained from actual user-end water consumption data from over 2.1 million customers over a 10-year period. These data were provided by the Water and Sewage Company of Greece (EYDAP). The large-scale experiment included the use of statistical models (ARIMA and SARIMA), deep learning [long short-term memory (LSTM)], and clustering techniques (k-NN algorithm). The model with the best performance was ARIMA, outperforming all the other models including the more complex ones. The LSTM model did not perform as expected in predicting water consumption, it excelled only in predicting the total amount of water consumed. These forecasts can be valuable tools for water utility companies, aiding in tasks such as customer billing and water balance calculations. This paper covers all the necessary steps that must be taken to achieve meaningful user-end water consumption forecasts, from raw data preprocessing to hyperparameter tuning for machine learning algorithms.

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.

Leveraging explainable AI for informed building retrofit decisions: Insights from a survey

Accurate predictions of building energy consumption are essential for reducing the energy performance gap. While data-driven energy quantification methods based on machine learning deliver promising results, the lack of Explainability prevents their widespread application. To overcome this, Explainable Artificial Intelligence (XAI) was introduced. However, to this point, no research has examined how effective these explanations are concerning decision-makers, i.e., property owners. To address this, we implement three transparent models (Linear Regression, Decision Tree, QLattice) and apply four XAI methods (Partial Dependency Plots, Accumulated Local Effects, Local Interpretable Model-Agnostic Explanations, Shapley Additive Explanations) to an Artificial Neural Network using a real-world dataset of 25,000 residential buildings. We evaluate their Prediction Accuracy and Explainability through a survey with 137 participants considering the human-centered dimensions of explanation satisfaction and perceived fidelity. The results quantify the Explainability-Accuracy trade-off in building energy consumption forecasting and how it can be counteracted by choosing the right XAI method to foster informed retrofit decisions. For research, we set the foundation for further increasing the Explainability of data-driven energy quantification methods and their human-centered evaluation. For practice, we encourage using XAI to reduce the acceptance gap of data-driven methods, whereby the XAI method should be selected carefully, as the Explainability within the methods varies by up to 10 %.

Fraud Detection of the Electricity Consumption by combining Deep Learning and Statistical Methods

An important issue for the electricity distribution companies is the non-technical loss (NTL), also known as electricity fraud. This issue has a significant impact on the economies of all countries in the world. In this context, we studied the problem of the imbalance between the electrical energy invoiced and the electrical energy supplied within the Algerian economic sector. This article presents an approach to detecting electrical fraud using a combination of Long Short-Term Memory (LSTM) and robust Exponential and Holt-Winters Smoothing (EHWS) methods in order to enhance the accuracy and efficacy of fraud detection mechanisms. The proposed approach investigates the fraudulent behaviour of electricity consumers and unfolds in several key phases. In the first step, monthly consumption forecasts are made and the model with the most accurate results is selected. Then, the phase of detection of anomalies in economic meters and detection of cases of fraud by economic customers begins. This phase relies on the robust exponential and Holt-Winters Smoothing methods for uncovering irregular patterns indicative of potential fraud instances. The proposed model was trained and evaluated, and several experiments were carried out using a large dataset of real users from the economic sector. The dataset comprised approximately 2,000 customers and encompassed 14 years of monthly electricity usage in Bejaia, Algeria. The results of the experiments demonstrate promising performance, underscoring the efficacy of our proposed solution in effectively detecting instances of fraud, leading us to conclude that this proposition is robust and can help improve the accuracy of locating abnormal consumer behaviour and increase the company's profits.

Carbon emissions path of public buildings based on LEAP model in Changsha city (China)

China is currently the world's top carbon emitter due to the acceleration of urbanization, the rapid growth of energy consumption and the rise in carbon emissions, with the construction industry leading the three energy sectors in terms of emission reduction potential. Among the various sectors, the construction industry holds immense potential for emission reduction, with public buildings playing a crucial role. In China, the expansion of public building spaces is closely linked to the pace of urbanization and contributes to a rising share of total building area. Public buildings, characterized by high energy usage, offer a great opportunity for energy conservation and emission reduction. Changsha is experiencing rapid growth and change, and the city is becoming more industrialized and urbanized, which leads to a prominent contradiction between energy supply and demand. There is also great potential for energy conservation and emission reduction in public buildings. Additionally, Changsha is an important node city in the Yangtze River urban agglomeration and the middle reaches of the Yangtze River Economic Belt. It is a typical area that has hot summer and cold winter, thus Changsha is selected as the research case to examine the carbon emission path of public buildings. The primary influencing factors of carbon emissions of public buildings in Changsha City are determined by using the LMDI factor decomposition method addition model. The scenario analysis method is adopted to set the two energy consumption forecast scenarios, which are the baseline scenario and the green scenario. Based on LEAP model and GREAT framework structure, the LEAP prediction model is constructed to predict the carbon emissions of public buildings in Changsha City from 2021 to 2035. The Baseline scenario indicates that while carbon emissions from public buildings in Changsha continue to rise, they are expected to decline starting in 2030. The peak carbon emission of 34.279 million tons of CO2 is predicted for 2032. In contrast, the Green scenario reveals a significantly slower growth rate in carbon emissions from public buildings, with the peak carbon emission projected to reach 27.054 million tons in 2030. This aligns with China's national goal. The findings of this study support the accuracy of the long-term prediction of the model, and offer recommendations for energy conservation and emission reduction in Changsha. This study also provides an example and certain guidance for other provinces and cities that have similar development models and climate conditions. Pertinent development strategies serve as a point of reference for other typical hot summer and cold winter regions.

Testing the Nonlinear Long- and Short-Run Distributional Asymmetries Effects of Bitcoin Prices on Bitcoin Energy Consumption: New Insights through the QNARDL Model and XGBoost Machine-Learning Tool

This study investigates the asymmetric impacts of Bitcoin prices on Bitcoin energy consumption. Two series are shown to be chaotic and non-linear using the BDS Independence test. To take into consideration this nonlinearity, we employed the QNARDL model as a traditional technique and Support Vector Machine (SVM) and eXtreme Gradient Boosting (XGBoost) as non-conventional approaches to study the link between Bitcoin energy usage and Bitcoin prices. Referring to QNARDL estimates, results show that the relationship between Bitcoin energy use and prices is asymmetric. Additionally, results demonstrate that changes in Bitcoin prices have a considerable effect, both short- and long-run, on energy consumption. As a result, any upsurge in the price of Bitcoin leads to an immediate boost in energy use. Furthermore, the short-term drop in Bitcoin values causes an increase in energy use. However, higher Bitcoin prices reduce energy use in the long run. Otherwise, every decline in Bitcoin prices leads to a long-term reduction in energy use. In addition, the performance metrics and convergence of the cost function provide evidence that the XGBoost model dominates the SVM model in terms of Bitcoin energy consumption forecasting. In addition, we analyze the effectiveness of several modeling approaches and discover that the XGBoost model (MSE: 0.52%; RMSE: 0.72 and R2: 96%) outperforms SVM (MSE: 4.89; RMSE: 2.21 and R2: 75%) in predicting. Results indicate that the forecast of Bitcoin energy consumption is more influenced by positive shocks to Bitcoin prices than negative shocks. This study gives insights into the policies that should be implemented, such as increasing the sustainable capacity, efficiency, and flexibility of mining operations, which would allow for the reduction of the negative impacts of Bitcoin price shocks on energy consumption.

Forecasts of sustainable consumption in small economies

Forecasts of sustainable consumption in small economies

Effectiveness of forecasters based on Neural Networks for Energy Management in Zero Energy Buildings

Energy management is an important challenge in Zero Energy Buildings (ZEB) with Photovoltaic (PV) generation systems. Measuring, Forecasting and Controlling Energy are important elements for energy management in buildings of all types, therefore, this research work focuses on the forecast stage of photovoltaic generation and energy consumption of the building. For this purpose, an analysis and comparison of five neural network techniques, namely Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM) single, dual and bidirectional modality and Gated Recurrent Units (GRU), were carried out. The results show that the MLP forecaster for photovoltaic energy generation turned out to be faster, however the dual-layer LSTM forecaster was more reliable and accurate with errors less than 1% between measured and forecast data. For energy consumption forecasting, the bidirectional LSTM forecaster turned out to be better. The evaluated forecasters turned out to be effective under homogeneous conditions of radiant sun, but they have effectiveness of 62% under conditions of cloudiness or solar intermittency, consequently failing to optimize the energy management of the ZEB system. This research work contributes to determining the best neural network technique as a forecaster for the energy management of ZEB as well as determining the impact and effectiveness of the forecaster in the face of solar intermittence. In addition, it provides an overview for decision making for those interested in developing smart electrical management systems for ZEB.

Comprehensive forecasting of California’s energy consumption: A multi-source and sectoral analysis using ARIMA and ARIMAX models

California’s significant role as the second-largest consumer of energy in the United States underscores the importance of accurate energy consumption predictions. With a thriving industrial sector, a burgeoning population, and ambitious environmental goals, the state’s energy landscape is dynamic and complex. This paper presents a comprehensive analysis of California’s energy consumption trends and provides detailed forecasting models for different energy sources and sectors. The study leverages ARIMA and ARIMAX models, considering both historical consumption data and exogenous variables. We address the unique challenges posed by the COVID-19 pandemic and the limited data for 2022, highlighting the resilience of these models in the face of uncertainty. Our analysis reveals that while fossil fuels continue to dominate California’s energy landscape, renewable energy sources, particularly solar and biomass, are experiencing substantial growth. Hydroelectric power, while sensitive to precipitation, remains a significant contributor to renewable energy consumption. Furthermore, we anticipate ongoing efforts to reduce fossil fuel consumption. The forecasts for energy consumption by sector suggest some decline in the commercial and residential sectors, reflecting California’s recently declining population and the shift away from brick-and-mortar shops and offices to online websites and remote work. In contrast, the industrial and transportation sectors are expected to experience some growth until they return to more constant pre-COVID levels.

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.

Bio-inspired bidirectional deep machine learning for real-time energy consumption forecasting and management

Accurately predicting electrical power demand is crucial to making related forecasts and to effective sustainable energy management. Most relevant state-of-the-art studies deploy models that do not use optimizing parameters and do not incorporate strategies for using forecast results. This study was designed to develop a novel electricity consumption forecasting model, the Symbiotic Bidirectional Gated Recurrent Unit, which integrates Gated Recurrent Unit, Bidirectional Technique, and Symbiotic Organisms Search algorithms. The results of tests on a series of evaluation criteria showed the proposed model performed significantly better than six comparison models when parameter optimization was used. For all three sector datasets, the proposed model generated the most-accurate predictions of all models. In practical terms, when supply is expected to exceed demand, the prediction results may be used to adjust power plant output to reduce wastage. Conversely, when demand is expected to exceed supply, Time-of-Use tariffs may be implemented based on time-of-day and seasonal fluctuations in demand to facilitate reductions in peak usage and level out overall demand.

Office building energy consumption forecast: Adaptive long short term memory networks driven by improved beluga whale optimization algorithm

With the development of urbanization, buildings have become a major source of energy consumption. This research uses a data-driven approach to achieve accurate building energy consumption prediction by analyzing and modeling building energy consumption data. The model proposed in this paper uses improved beluga whale optimization algorithm (IBWO) to optimize long short-term memory networks (LSTM) for accurate energy consumption prediction. In order to enhance the ability of BWO in global search and local exploitation, a new method of dynamic adjustment of step factor as well as strategies such as nonlinear decreasing are introduced to improve BWO. For the first time, it is proposed to explore the accuracy of the number of hyper-parameters of LSTM on the prediction of energy consumption, and the improved beluga whale optimization algorithm is used to optimize the two, three, and four hyper-parameters of LSTM respectively. Then short-term prediction of historical energy consumption data of an office building in Xi'a is performed. Experiments show that the optimization of the four hyperparameters of LSTM using the IBWO of this paper can reduce the mean absolute error (MAE) of the pre-improvement model from 830.71 KW to 128.28 KW, the mean absolute percentage error (MAPE) from 12.32 % to 1.38 %, and the coefficient of variation (CV) from 7.5 % to 1.2 %.

Comparative assessment and policy analysis of forecasting quarterly renewable energy demand: Fresh evidence from an innovative seasonal approach with superior matching algorithms

In the pursuit of sustainable development, accurate renewable energy demand forecasting holds great significance for climate change mitigation and promoting sustainability. However, renewable energy forecasting has been consistently challenged by seasonality and nonlinearity. Identifying the periodic and nonlinear characteristics concealed within renewable energy sources accurately is still an unexplored problem. Consequently, an innovative nonlinear discrete seasonal grey model is proposed for renewable energy forecasting, which incorporates seasonal dummy variables and a power exponent term for handling the seasonality and nonlinear patterns in time series. Furthermore, an intelligent algorithm matching framework is proposed to augment the flexibility of the newly developed model. For practical purposes, the new methodology is contrasted against a range of benchmarks encompassing statistical, machine-learning, and traditional grey models in forecasting the quarterly total renewable energy consumption in the United States. The proposed model exhibits over 27% improvement rates over its counterparts, achieving the most superior predictive accuracies of 1.45%, 39.27, and 0.79 in MAPEP, RMSEP, and MASEP metrics, respectively. Furthermore, the probability density and sample size analyses are conducted to validate the robustness of the new model, confirming its adaptability and stability towards algorithm randomness and historical information volume. Consequently, the novel model is employed to forecast the short-to-long-terms renewable energy consumption in the U.S., showcasing an upward trend and seasonal fluctuations of the consumption for the forthcoming 24 quarters from 2023Q4 to 2029Q3. These insights can offer valuable implications to the stakeholders such as energy suppliers, utility managers, and policy advocates, highlighting actionable strategies for optimizing renewable energy consumption forecasting and aiding sustainable development initiatives.