Energy Demand Forecasting Research Articles

Enhancing the Predictability of Wintertime Energy Demand in The Netherlands Using Ensemble Model Prophet-LSTM

Energy demand forecasting is crucial for maintaining stable and affordable energy supplies, especially for vulnerable populations most affected by shortages and high costs. In the Netherlands, transmission system operator TenneT has raised concerns about potential electricity shortages by 2030. Rising energy prices and the impact of climate change on the energy demand further complicate today’s energy market. Policymakers lack clear insights into demand patterns, which complicates the optimization of energy use and the protection of at-risk communities. Accurate and timely forecasts are essential for addressing these issues and supporting sustainable energy management. This research focuses on enhancing the accuracy and lead time of wintertime energy demand forecasts in the Netherlands using advanced machine learning. The ensemble model Prophet-LSTM is trained on hourly load consumption data combined with climate change-related and energy price predictors. The results demonstrate significant improvements over baseline models, achieving a Pearson correlation coefficient of r=0.93 compared to r=0.50 in prior studies, as well as accurate forecasts up to 180 days ahead, compared to 2 months. Incorporating climate change-related predictors is challenging due to multicollinearity, highlighting the importance of careful predictor selection. Including energy price predictors yielded modest yet hopeful results, suggesting their ability to optimize energy demand forecasting.

Mathematical modeling of Ethiopia's energy demand by sectors and energy types, with forecasts for the next 30 years

Due to the scarcity of economic resources, it is vital to optimize everything so that the supply and demand lines intersect in an optimized quantity, with no shortage or surplus of a provided item or service. Energy supply contains both surplus and shortage, thus estimating the amount of projected energy demand is a key work that must be completed. The objective of this paper is generating a mathematical model based on the actual data for thirty years forecasting. To create a mathematical model using actual data from the last fifteen years, a model that can represent the past trend and be used for future forecasting. This study provides a general overview of Ethiopia's current energy requirement with different energy type as well as sector-specific energy demand and estimates for different economic growth scenarios up to 2052. This model was created using a linear regression polynomial fit through Origin graphic and analysis software, and an econometric model was also applied. GDP was utilized as an independent variable in the economic model to determine the trend of energy consumption. Another input-output model is also used for multilinear regression to evaluate the change of four variables, GDP, population growth, urbanization growth rate, and general inflation rate, which was quantitatively linked with total energy requirement using Weka software. The mathematical model developed through linear and multilinear regression has been validated by using a different assumption on GDP growth based on past growth rate as low, medium and high growth rate and using the mathematical formula to generate an energy demand trend that can be compared with the actual trend; as a result, all the mathematical model that are generated has been found to be valid for the purpose of the intended work. Based on the generated mathematical model and different GDP growth rate scenario as low, medium, business as usual (BAU) and high a future energy demand was forecasted up to 2052 for thirty years. The model's results can help energy planners ensure that the country's supply capacity keeps up with predicted energy demand growth.

Artificial Neural Networks for Energy Demand Prediction in an Economic MPC‐Based Energy Management System

ABSTRACTMicrogrids are a development trend and have attracted a lot of attention worldwide. The control system plays a crucial role in implementing these systems and, due to their complexity, artificial intelligence techniques represent some enabling technologies for their future development and success. In this paper, we propose a novel formulation of an economic model predictive control (economic MPC) applied to a microgrid designed for a faculty building with the inclusion of a predictive model to deal with the energy demand disturbance using a recurrent neural network of the long short‐term memory (RNN‐LSTM). First, we develop a framework to identify an RNN‐LSTM using historical data registered by a smart three‐phase power quality analyzer to provide feedforward power demand predictions. Next, we present an economic MPC formulation that includes the prediction model for the disturbance within the optimization problem to be solved by the MPC strategy. We carried out simulations with different scenarios of energy consumption, available resources, and simulation times to highlight the results obtained and analyze the performance of the energy management system. In all cases, we observed the correct operation of the proposed control scheme, complying at all times with the objectives and operational restrictions imposed on the system.

Recurrence Multilinear Regression Technique for Improving Accuracy of Energy Prediction in Power Systems

This paper demonstrates how artificial intelligence can be implemented in order to predict the energy needs of daily households using both multilinear regression (MLR) and single linear regression (SLR) methods. As a basic implementation, the SLR makes use of one input variable, which is the total amount of energy generated as an input. The MLR implementation involves multiple input variables being taken from various energy sources, including gas, coal, geothermal, wind, water, biomass, oil, etc. All of these variables are derived from detailed energy production data from the various energy sources. The purpose of this paper is to demonstrate that it is possible to analyze energy demand and supply directly together as a way to produce a more in-depth analysis. By analyzing energy production data from previous periods of time, a prediction of energy demand can be made. Compared to the SLR implementation, the MLR implementation is found to perform better because it is able to achieve a smaller error value. Furthermore, the forecasting pattern is carried out sequentially based on a periodic pattern, so this paper calls this method the recurrence multilinear regression (RMLR) method. This paper also creates a pre-clustering using the K-Means algorithm before the energy prediction to improve accuracy. Other models such as exponential GPR, sequential XGBoost, and seq2seq LSTM are used for comparison. The prediction results are evaluated by calculating the MAE, RMSE, MAPE, MAPA, and time execution for all models. The simulation results show that the fastest and best model that obtains the smallest error (3.4%) is the RMLR clustered using a weekly pattern period.

Optimizing Distributed Energy Resources in Microgrid SCUC through Seq2Seq Scheduling Algorithms

In order to optimize Distributed Energy Resources (DERs) inside a microgrid's Security-Constrained Unit Commitment framework, this study investigates the use of Seq2Seq (Sequence-to-Sequence) scheduling methods (SCUC). There is a growing consensus that microgrids are an important part of the future of the electric grid because of the advantages they provide in terms of reliability, renewable energy integration, and overall efficiency. In the context of complicated SCUC issues, efficient scheduling and optimization of DERs are essential for realizing their full potential. Seq2Seq models, a kind of deep learning approach, have shown impressive performance in several applications requiring sequence prediction. Uncertainty in renewable energy production, energy demand forecasts, and security limitations are all addressed in this work as Seq2Seq algorithms are applied to microgrid SCUC. Significant gains in economic efficiency, ecological viability, and compliance with security constraints have been shown. This study lays the way for the development of more effective, sustainable, and resilient energy infrastructure by contributing to the advancement of the area of microgrid optimization.

Spread efficiency of energy demand in the industrial chain: a perspective from economic distance

This paper investigates the spread efficiency of energy demand in the industrial chain based on the input-output method. The concept of intersectoral economic distance and spread efficiency was introduced to formulate the energy demand spread functions. Then, an empirical study was conducted based on China's 2017 input-output data and nearly 3,500 companies were listed in the Chinese A-share market. The results show that the sectoral average spread time is 32.75 days. Manufacturing plays a central role in the spread of energy demand. In the short term, the logistics sector has the greatest pulling effect on energy demand, while the electric power sector has the greatest impact on energy demand in the long term. Energy demand caused by exports is greater than consumption and investment. Finally, sectoral economic distances and actual spread functions for 9 main energy sources are provided to help us better predict energy demand.

Energy Demand Estimation in Turkey According to Road and Rail Transportation: Walrus Optimizer and White Shark Optimizer Algorithm-Based Model Development and Application

Transport energy demand (TED) forecasting is a crucial issue for countries like Turkey that are dependent on external resources. The accuracy and effectiveness of these forecasts are extremely important, especially for the strategies and plans to be developed. With this in mind, different forms of forecasting models were developed in the present study using the Walrus Optimizer (WO) and White Shark Optimizer (WSO) algorithms to estimate Turkey’s energy consumption related to road and railway transportation modes. Additionally, another objective of this study was to examine the impacts of different transport modes on energy demand. To investigate the effect of demand distribution among transport modes on energy consumption, model parameters such as passenger-kilometers (P-km), freight-kilometers (F-km), carbon dioxide emissions (CO2), gross domestic product (GDP), and population (POP) were utilized in the development of the models. It was found that the WO algorithm outperformed the WSO algorithm and was the most suitable method for energy demand forecasting. All the developed models demonstrated a better performance level than those reported in previous studies, with the best performance achieved by the semi-quadratic model developed with the WO, showing a 0.95% MAPE value. Projections for energy demand up to the year 2035 were established based on two different scenarios: the current demand distribution among transport modes, and a demand shift from road to rail transportation. It is anticipated that the proposed energy demand models will serve as an important guide for effective planning and strategy development. Moreover, the findings suggest that a balanced distribution among transport modes will have a positive impact on transport energy and will result in lower energy requirements.

Optimizing Distributed Energy Resources in Microgrid SCUC through Seq2Seq Scheduling Algorithms

In order to optimize Distributed Energy Resources (DERs) inside a microgrid's Security-Constrained Unit Commitment framework, this study investigates the use of Seq2Seq (Sequence-to-Sequence) scheduling methods (SCUC). There is a growing consensus that microgrids are an important part of the future of the electric grid because of the advantages they provide in terms of reliability, renewable energy integration, and overall efficiency. In the context of complicated SCUC issues, efficient scheduling and optimization of DERs are essential for realizing their full potential. Seq2Seq models, a kind of deep learning approach, have shown impressive performance in several applications requiring sequence prediction. Uncertainty in renewable energy production, energy demand forecasts, and security limitations are all addressed in this work as Seq2Seq algorithms are applied to microgrid SCUC. Significant gains in economic efficiency, ecological viability, and compliance with security constraints have been shown. This study lays the way for the development of more effective, sustainable, and resilient energy infrastructure by contributing to the advancement of the area of microgrid optimization.

Techno-Economic Optimization of Microgrid Operation with Integration of Renewable Energy, Hydrogen Storage, and Micro Gas Turbine

Microgrids are integral to modern energy systems, yet they face substantial challenges in integrating diverse components, managing complex dynamics, and ensuring stability amid renewable energy variability. This study investigates the integration of wind turbines, an electrolyzer, and a hydrogen-compatible micro gas turbine (MGT), with a focus on enhancing operational efficiency and maintaining dynamic equilibrium within the microgrid. The synergy between the electrolyzer and MGT provides a robust energy storage solution, improving both system stability and performance.Using advanced machine learning and real operational data, this research generates highly accurate, rapid models with greater precision and detail than conventional methods. The intelligent management system developed combines real-time optimization and adaptive fine-tuning, using forecasts of weather, electricity prices, and energy demand to devise optimized operational strategies. The study systematically analyzes various configurations, including grid-connected and island modes, and evaluates the impact of hydrogen storage in each scenario.The results demonstrate that the optimization approach substantially enhances economic returns. However, this can also lead to increased natural gas consumption and emissions, revealing a trade-off between financial gains and environmental sustainability. This highlights the necessity for strategies that reconcile economic incentives with sustainability objectives, offering valuable insights for improved microgrid management.

An Integrated Artificial Intelligence Approach for Building Energy Demand Forecasting

Buildings are complex assets, characterized by environments and uses that change over time, variable occupancies, and long life cycles. They have high operational costs, mostly due to their energy requirements, and account for 30% to 40% of global greenhouse gas emissions. Consequently, substantial effort has been made to forecast their energy needs, with the scope of optimizing their economic and environmental impact. In this regard, the available literature focuses mainly on short-term modeling through the implementation of sets of physics-based equations (i.e., white-box), functional relationships between input and output variables (i.e., black-box), or a combination of both (i.e., grey-box). On the other hand, more research is required on long-term forecast models with the aim of reducing the energy needs. Within this context, this article presents an original automatic procedure for forecasting the energy needs of buildings in short- and long-term time horizons. This is accomplished by scaling an unknown facility from a similar facility that is already known and by executing a black-box approach based on machine learning algorithms. The proposed method is implemented in real case studies in Italy, predicting the energy needs (i.e., heating, cooling, and electricity) of Sant’Anna Hospital in Ferrara using the historical data of Ca’ Foncello Hospital in Treviso. The results show an adjusted coefficient of determination above 0.7 and an average error below 10% for all the energy vectors, demonstrating a feasible forecast performance with a low training set-to-test set ratio.

Leveraging machine learning to optimize renewable energy integration in developing economies

The integration of renewable energy sources into power grids is a critical challenge for developing economies, where infrastructure limitations, unpredictable energy demand, and policy gaps hinder effective energy transitions. Machine learning (ML) offers transformative potential in addressing these challenges, enabling more efficient and reliable energy systems through advanced data analytics, predictive modeling, and real-time decision-making. This review explores how ML can optimize renewable energy integration by improving forecasting accuracy, enhancing grid stability, and optimizing resource allocation in solar, wind, and hydropower systems. Machine learning algorithms are particularly effective in predicting energy demand and renewable resource availability, allowing for better alignment between energy supply and consumption patterns. By leveraging ML-based predictive models, grid operators can mitigate the risks of energy shortages or oversupply, improve grid stability, and reduce operational costs. Furthermore, the application of ML in renewable energy systems provides opportunities for developing economies to leapfrog traditional energy infrastructure limitations by adopting smart grids that integrate real-time data to enhance decision-making and efficiency. This review also reviews case studies from Africa and Latin America, highlighting successful implementations of ML in renewable energy systems. These examples underscore the potential for ML to accelerate the deployment of sustainable energy solutions, while also addressing technical, economic, and policy barriers that exist in developing contexts. With continued advancements in machine learning, combined with supportive regulatory frameworks and investment in digital infrastructure, developing economies have the potential to realize substantial gains in renewable energy integration. This review concludes by discussing future trends, challenges, and opportunities for leveraging machine learning to optimize renewable energy integration, ultimately contributing to sustainable development and energy security in emerging markets.

Energy system optimization based on fuzzy decision support system and unstructured data

To address the complex challenges in energy systems, this study proposes a novel optimization framework that integrates fuzzy decision support and unstructured data processing technologies. This framework aims to improve efficiency, reduce costs, decrease environmental impact, increase system flexibility, and enhance user satisfaction, thereby promoting sustainable development in the energy industry. The framework combines the innovative Energy Semantic Mapping Model (ESMM) and the advanced deep learning architecture ResNet to process textual and visual data effectively. ESMM enables accurate prediction of energy demand, while ResNet significantly reduces equipment maintenance costs and improves energy distribution efficiency. These advancements are critical as they address the limitations of existing approaches in handling large-scale unstructured data and making informed decisions under uncertainty. The Environmental Impact Assessment (EIA) confirms the model's effectiveness in reducing carbon emissions. A comprehensive economic analysis demonstrates substantial cost savings in energy procurement and operations and maintenance, with overall savings exceeding 25%. Enhanced user satisfaction and reduced system response times further validate the practical utility of the proposed approach. Additionally, a genetic algorithm is used to optimize the fuzzy rule base, enhancing the robustness and adaptability of the model. Experimental results show superior performance compared to traditional systems, providing strong empirical evidence for the intelligent transformation of energy systems. This research contributes to the field by offering a more sophisticated and flexible solution for managing energy systems, particularly in terms of leveraging unstructured data and improving decision-making processes.

Economic development through sustainable energy transition: the impact of machine learning-driven energy efficiency in industrial sectors

The transition to sustainable energy systems is a critical component in addressing global environmental challenges and fostering economic development. Industrial sectors, which are major consumers of energy and contributors to greenhouse gas emissions, play a pivotal role in this transition. Machine learning (ML) technologies have emerged as transformative tools for enhancing energy efficiency in these sectors, offering significant potential for economic and environmental benefits. This review explores the impact of ML-driven energy efficiency on economic development, focusing on its application within various industrial contexts. The integration of machine learning into energy management systems allows for advanced predictive analytics, real-time monitoring, and process optimization. ML algorithms can analyze large datasets to forecast energy demand, detect inefficiencies, and optimize operational processes, leading to substantial cost savings and energy reductions. For example, in manufacturing industries, ML-driven solutions have been employed to enhance predictive maintenance, optimize production processes, and reduce energy consumption, resulting in lower operational costs and increased productivity. Similarly, in industrial facilities such as chemical plants and data centers, ML has been used to streamline energy management and minimize waste, demonstrating its versatility and effectiveness across diverse industrial applications. The economic impacts of ML-driven energy efficiency are profound. By reducing energy costs and improving operational performance, industries can achieve significant financial savings, which in turn can be reinvested into further innovations and growth. Additionally, the adoption of ML technologies fosters job creation and skill development in the tech and energy sectors, contributing to broader economic growth. Industries that leverage ML for energy efficiency gain a competitive advantage, positioning themselves favorably in the market and tapping into new opportunities for innovation and expansion. However, the transition to ML-driven energy efficiency is not without challenges. Issues related to data availability and quality, high implementation costs, and industry-specific constraints must be addressed to fully realize the benefits. Policymakers play a crucial role in facilitating this transition by developing supportive policies and regulations that encourage the adoption of ML technologies in energy management. Machine learning offers a transformative approach to achieving energy efficiency in industrial sectors, with significant implications for economic development. By addressing the challenges and leveraging emerging opportunities, industries and policymakers can drive sustainable energy transitions that benefit both the environment and the economy. This review provides a comprehensive analysis of these impacts and offers strategic recommendations for advancing ML-driven energy efficiency in the industrial sector. Keywords: Economic Development, Sustainable Energy, Machine Learning, Industrial Sectors.

Medium and long-term energy demand forecasts by sectors in China under the goal of “carbon peaking & carbon neutrality”: Based on the LEAP-China model

Energy is a critical material foundation for sustainable economic and social development and national security, and it is of great significance to explore China's medium and long-term energy demand to realize the “dual-carbon” goal. Based on the current state of economic and social development and energy consumption, the LEAP-CHINA model is constructed to create baseline scenario, structural adjustment scenario, technology abatement scenario, and comprehensive scenario to forecast China's total energy demand, end-use industry subsectors, end-use energy subvarieties, processing and conversion sectors from 2022 to 2060. The results show: (1) China's total energy demand showed a “rapid increase before 2039, a slow increase between 2039 and 2049, and a gentle decrease after peaking in 2049". (2) The contribution rate of energy conservation and emission reduction was comprehensive scenario > technology abatement scenario > industrial adjustment scenario > baseline scenario > energy adjustment scenario. (3) Industry was the largest energy demand industry, and the rest of the industries' energy demand share was decreasing. (4) There is a definite trend towards power system decarbonization and energy system electrification, with non-fossil energy power generation anticipated to increase to 78%–82 % in 2060. Finally, it proposes countermeasures for China's energy development in the medium and long-term.

Green Energy Management in Manufacturing Based on Demand Prediction by Artificial Intelligence—A Review

Energy efficiency in production systems and processes is a key global research topic, especially in light of the Green Deal, Industry 4.0/5.0 paradigms, and rising energy prices. Research on improving the energy efficiency of production based on artificial intelligence (AI) analysis brings promising solutions, and the digital transformation of industry towards green energy is slowly becoming a reality. New production planning rules, the optimization of the use of the Industrial Internet of Things (IIoT), industrial cyber-physical systems (ICPSs), and the effective use of production data and their optimization with AI bring further opportunities for sustainable, energy-efficient production. The aim of this study is to systematically evaluate and quantify the research results, trends, and research impact on energy management in production based on AI-based demand forecasting. The value of the research includes the broader use of AI which will reduce the impact of the observed environmental and economic problems in the areas of reducing energy consumption, forecasting accuracy, and production efficiency. In addition, the demand for Green AI technologies in creating sustainable solutions, reducing the impact of AI on the environment, and improving the accuracy of forecasts, including in the area of optimization of electricity storage, will increase. A key emerging research trend in green energy management in manufacturing is the use of AI-based demand forecasting to optimize energy consumption, reduce waste, and increase sustainability. An innovative perspective that leverages AI’s ability to accurately forecast energy demand allows manufacturers to align energy consumption with production schedules, minimizing excess energy consumption and emissions. Advanced machine learning (ML) algorithms can integrate real-time data from various sources, such as weather patterns and market demand, to improve forecast accuracy. This supports both sustainability and economic efficiency. In addition, AI-based demand forecasting can enable more dynamic and responsive energy management systems, paving the way for smarter, more resilient manufacturing processes. The paper’s contribution goes beyond mere description, making analyses, comparisons, and generalizations based on the leading current literature, logical conclusions from the state-of-the-art, and the authors’ knowledge and experience in renewable energy, AI, and mechatronics.

Optimal operational planning of a bio-fuelled cogeneration plant: Integration of sparse nonlinear dynamics identification and deep reinforcement learning

This paper presents a novel data-driven approach for short-term operational planning of a cogeneration plant. The proposed methodology utilizes sparse identification of nonlinear dynamics (SINDy) to extract a dynamic model of heat generation from operational data. This model is then employed to simulate the plant dynamics during the training of a reinforcement learning (RL) agent, enabling online stochastic optimization of the production plan in real-time. The incorporation of SINDy enhances the accuracy of capturing the plant's nonlinear dynamics and significantly improves the computational speed of plant simulations, enabling efficient RL agent training within a reasonable timeframe. The performance of operational planning with the RL agent is compared to that of dynamic programming, a widely used method in the literature. The evaluation metric encompasses energy efficiency, unmet demands, and wasted heat. The comparison investigates the effectiveness of RL and dynamic programming under various scenarios with different qualities of energy demand forecasts. The RL agent exhibits robustness and notably improves the operational planning performance, particularly when faced with uncertain energy demands in the environment. Furthermore, the findings show that the RL agent, trained on a school building data, could successfully perform planning tasks for a hotel building, indicating the transferability of learned planning knowledge across different cogeneration use cases.

An artificial intelligence framework for explainable drift detection in energy forecasting

Accurate energy consumption forecasting is crucial for reducing operational costs, achieving net-zero carbon emissions, and ensuring sustainable buildings and cities of the future. Despite the frequent use of Artificial Intelligence (AI) algorithms for learning energy consumption patterns and predictions in Building Science, relying solely on these techniques for energy demand prediction addresses only a fraction of the challenge. A drift in energy usage can lead to inaccuracies in these AI models and subsequently to poor decision-making and interventions. While drift detection techniques have been reported, a reliable and robust approach capable of explaining identified discrepancies with actionable insights has not been discussed in extant literature. Hence, this paper presents an Artificial Intelligence framework for energy consumption forecasting with explainable drift detection, aimed at addressing these challenges. The proposed framework is composed of energy embeddings, an optimized dimensional model integrated within a data warehouse, and scalable cloud implementation for effective drift detection with explainability capability. The framework is empirically evaluated in the real-world setting of a multi-campus, mixed-use tertiary education setting in Victoria, Australia. The results of these experiments highlight its capabilities in detecting concept drift, adapting forecast predictions, and providing an interpretation of the changes using energy embeddings.

Prediction of Energy Demand of Customer Microgrid Based on Multi-Level Fuzzy Evaluation Model

In order to improve the accuracy of customer microgrid energy demand forecasting, this paper conducts an in-depth study on the prediction of customer microgrid energy demand based on a multi-level fuzzy evaluation model. Firstly, a multi-level fuzzy evaluation index system was constructed, the multi-level fuzzy evaluation indicators were clarified, and the correlation characteristics, interactive characteristics and data dimension characteristics of customer energy consumption data were analyzed. Then, according to the principle of multi-level fuzzy evaluation method, the factor set and the evaluation set are clarified, and the multi-level fuzzy relationship is induced by the multi-level fuzzy mapping, the fuzzy matrix is obtained, and the identification parameters are processed to complete the prediction of the customer's microgrid energy demand. The simulation results show that the overall prediction accuracy of the proposed model is high, the prediction time is short, and the practicability is strong.

Sustainable machining of Inconel 718 using minimum quantity lubrication: Artificial intelligence-based process modelling

Governments and industries are developing aggressive policies to reduce carbon emissions and shift from fossil fuels to renewable energy. On the other hand, industries struggle to reduce energy consumption and depend on production lot sizes to control energy requirements. In this regard, energy-efficient processing through CNC machine tools can potentially influence energy demand and requires energy-aware power consumption strategies for machining processes. For manufacturing a single product, predicting energy demand can be decisive in determining parametric control and other factors. Previously analytical models have been largely used to model machining requirements and energy demand. However, these models largely depend on parameterization and do not facilitate the integration of external sub-systems. Therefore, in this paper, an artificial intelligence-based power reduction strategy is developed and implemented on single material (Inconel 718), four control parameters (cutting speed, feed rate, depth of cut and flow rate) and two sub-systems (minimum quantity lubrication (MQL) and nanofluids-based minimum quantity lubrication (NF-MQL)). The paper employs four machine learning algorithms,’ K-Nearest Neighbor’, ‘Gaussian Regression’, ‘Decision Tree’, and ‘Logistic Regression’, to evaluate their functionality in predicting power consumption (Pc) of CNC machining systems using a real experimental data set. As per evaluation based on five performance metrics (R-square, MSE, RMSE, MAE, and MedAE), ‘Decision Tree’ has achieved the most accurate power consumption predictions. The comparative results highlight ‘Decision Tree’ as the most better predictor with the optimal max_depth of 2 showing Pc MQL R2 of 0.915 and Pc NF-MQL R2 of 0.931.

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.