Cost Efficient & Scalable Blockchain Architecture for Cloud Computing Integrity Auditing

Abstract

In today’s world, cloud computing has become the primary choice for data storage and management. However, the security of data stored in a distributed environment via the cloud remains a key challenge. The traditional approaches to the verification of integrity rely heavily on the use of centralized architectures, which result in lack of transparency, trust, and vulnerability to failure points. Blockchain technology has been proposed as a decentralized means for securing data integrity in a cloud computing environment due to those limitations. It becomes clear that blockchain technology allows us to improve security and transparency significantly; however, at the moment, all existing solutions tend to consider only certain aspects of the problem such as auditing, optimization of the storage space, etc. Most critical insights obtained in the research include the issues of high cost, large overhead, limited scalability, and complicated architecture. Moreover, most studies do not have adequate cost considerations and practical validation regarding implementation. Based on the above analysis, this paper proposes important areas for further study, such as the development of architectures that are scalable, cost-effective, and feature data integrity schemes. In addition, the research suggests the need for a unified solution that will integrate sharding, off-chaining, and layer-two methods for massive cloud infrastructures.

Introduction

Electrical load forecasting predicts future electricity demand to support efficient power system operation, generation scheduling, energy trading, and infrastructure planning. The growing adoption of smart grids, distributed energy resources (DERs), electric vehicles (EVs), demand response programs, and prosumers has made electricity demand more dynamic and nonlinear, reducing the effectiveness of traditional forecasting techniques.

Conventional statistical methods such as regression, ARIMA, SARIMA, exponential smoothing, and Kalman filters are computationally efficient but struggle to model nonlinear and non-stationary load patterns. Recent advances in artificial intelligence, machine learning, and deep learning have significantly improved forecasting accuracy. Models including Artificial Neural Networks (ANNs), Support Vector Regression (SVR), Random Forest, XGBoost, LightGBM, Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Transformer-based architectures achieve Mean Absolute Percentage Error (MAPE) values below 2% in short-term forecasting.

Load forecasting is categorized into Short-Term Load Forecasting (STLF), Medium-Term Load Forecasting (MTLF), and Long-Term Load Forecasting (LTLF). STLF supports real-time grid operations, MTLF assists maintenance scheduling and energy market planning, while LTLF is used for long-term infrastructure development and energy policy decisions. Each forecasting horizon requires different input features and modeling strategies.

Electricity demand is influenced by four major groups of factors: weather and climate (especially temperature), economic and demographic variables (GDP, industrial activity, population growth), calendar and temporal effects (time of day, weekdays, holidays, and seasons), and social and behavioral factors such as consumer lifestyles, smart homes, IoT devices, and Home Energy Management Systems (HEMS). Integrating these diverse factors significantly improves forecasting performance.

The paper reviews 72 research publications from 2000–2025, covering statistical, AI-based, machine learning, deep learning, hybrid, ensemble, federated learning, explainable AI, and smart grid forecasting methods. Hybrid approaches combining statistical and AI techniques improve robustness by capturing both linear and nonlinear relationships.

Among modern methods, deep learning models—particularly LSTM, GRU, CNN-LSTM hybrids, and Transformer architectures—demonstrate the highest forecasting accuracy due to their ability to learn complex temporal dependencies from large datasets. Ensemble machine learning methods such as Random Forest and XGBoost also provide strong predictive performance with improved feature selection and reduced overfitting.

Conclusion

Electrical load forecasting predicts future electricity demand to support efficient power system operation, generation scheduling, energy trading, and infrastructure planning. The growing adoption of smart grids, distributed energy resources (DERs), electric vehicles (EVs), demand response programs, and prosumers has made electricity demand more dynamic and nonlinear, reducing the effectiveness of traditional forecasting techniques. Conventional statistical methods such as regression, ARIMA, SARIMA, exponential smoothing, and Kalman filters are computationally efficient but struggle to model nonlinear and non-stationary load patterns. Recent advances in artificial intelligence, machine learning, and deep learning have significantly improved forecasting accuracy. Models including Artificial Neural Networks (ANNs), Support Vector Regression (SVR), Random Forest, XGBoost, LightGBM, Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Transformer-based architectures achieve Mean Absolute Percentage Error (MAPE) values below 2% in short-term forecasting. Load forecasting is categorized into Short-Term Load Forecasting (STLF), Medium-Term Load Forecasting (MTLF), and Long-Term Load Forecasting (LTLF). STLF supports real-time grid operations, MTLF assists maintenance scheduling and energy market planning, while LTLF is used for long-term infrastructure development and energy policy decisions. Each forecasting horizon requires different input features and modeling strategies. Electricity demand is influenced by four major groups of factors: weather and climate (especially temperature), economic and demographic variables (GDP, industrial activity, population growth), calendar and temporal effects (time of day, weekdays, holidays, and seasons), and social and behavioral factors such as consumer lifestyles, smart homes, IoT devices, and Home Energy Management Systems (HEMS). Integrating these diverse factors significantly improves forecasting performance. The paper reviews 72 research publications from 2000–2025, covering statistical, AI-based, machine learning, deep learning, hybrid, ensemble, federated learning, explainable AI, and smart grid forecasting methods. Hybrid approaches combining statistical and AI techniques improve robustness by capturing both linear and nonlinear relationships. Among modern methods, deep learning models—particularly LSTM, GRU, CNN-LSTM hybrids, and Transformer architectures—demonstrate the highest forecasting accuracy due to their ability to learn complex temporal dependencies from large datasets. Ensemble machine learning methods such as Random Forest and XGBoost also provide strong predictive performance with improved feature selection and reduced overfitting.

References

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Copyright

Copyright © 2026 Mr. Kunal Mohan Pawar, Ms. Pooja Tupe. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.