Jul 2026· International Scientific Journal of Engineering and Management· Vol 05, pp. 1-9· 0 citations
TL;DR
The study shows that machine learning enables smarter load balancing, better integration of renewable energy, and improved decision-making in power distribution systems, and supports the development of intelligent, sustainable, and data-driven energy.
Abstract
Accurate forecasting of power consumption is critical for efficient energy management, grid stability, and cost reduction. This study explores the application of advanced machine learning models to predict short-term and long-term power usage patterns. By leveraging historical consumption data alongside relevant external factors such as weather conditions, time of day, and economic indicators, the proposed approach employs algorithms including Random Forest, Support Vector Machines, and Deep Learning networks. The models are trained and validated on real-world datasets to evaluate their predictive accuracy and robustness. Results demonstrate that machine learning techniques significantly improve forecasting precision compared to traditional statistical methods. This enables smarter energy distribution, better demand response strategies, and supports the integration of renewable energy sources, thereby contributing to sustainable power system operation. Traditional statistical methods often fall short in capturing the complex, non-linear patterns of modern electricity usage. This project explores advanced machine learning techniques such as Random Forest, Support Vector Machines, and LSTM networks to predict both short-term and long- term electricity demand. By leveraging historical data, weather conditions, time-based factors, and user behavior, these models demonstrate superior forecasting performance compared to conventional methods. The study shows that machine learning enables smarter load balancing, better integration of renewable energy, and improved decision-making in power distribution systems. The proposed approach supports the development of intelligent, sustainable, and data-driven energy.
Keywords: Power Consumption Forecasting, Machine Learning, Random Forest, LSTM, Smart Grid, Energy Management, Electricity Demand Prediction, Time Series Forecasting.
Accurate forecast of electricity demand has become essential for modern energy systems, which are Face-off escalating challenges because of industrial expansion, population growth, and the incorporation of renewable energy sources. With the development of machine learning methods, one can now efficiently forecast power consumption with the help of past data. This paper introduces a machine learning-based predictor of power consumption. We analyze various machine learning techniques, i.e., random forest, XGBoost, linear regression for power forecasting, in this work. These models were trained and tested on historical electricity consumption data from the Ministry of Electricity of Iraq, 2022 to 2025. The models' performance was evaluated using a number of metrics, such as Mean Absolute Error, Root Mean Squared Error, Mean absolute percentage error, and R-squared. the best performance model for demand was XGBoost, the model achieved on R-squared value of 0.98 and Linear regression model achieved on R-squared value of 0.98 for supply.
Maryam Jamal Abdulhameed, Ali Hasan Taresh· Iraqi Journal for Computers...· 0 citations
The intermittent and volatile characteristics of new energy generation, together with the increasing demand for stable power supply in intelligent industrial systems, make accurate forecasting a critical issue for grid dispatch and electromagnetic energy management. This study systematically reviews the technological evolution of time series analysis methods for wind and photovoltaic (PV) power forecasting and establishes a comparative framework covering classical statistical models, intelligent learning algorithms, and hybrid modeling strategies. Based on two years of operational data collected from an actual wind farm and PV station in East China, the forecasting performance of ARIMA, exponential smoothing, Support Vector Regression (SVR), Long Short-Term Memory (LSTM) networks, and Transformer architectures is comprehensively evaluated, while hybrid approaches based on Empirical Mode Decomposition (EMD) are further investigated. The results demonstrate that model selection should jointly consider forecasting horizon, data characteristics, and computational constraints. Classical statistical methods remain robust under stable operating conditions but are less effective in capturing extreme fluctuations, whereas deep learning approaches exhibit superior capability in modeling long-range temporal dependencies despite reduced interpretability. Decomposition-based hybrid strategies achieve a more balanced performance across diverse scenarios and show enhanced robustness under extreme weather conditions. The study further proposes a structured model selection guideline by matching data characteristics with operational requirements, providing theoretical support for forecasting system design in renewable-energy-driven power networks and offering useful references for electromagnetic energy utilization and intelligent industrial applications.
M. S. Song, C. Yang, Z. Heng et al.· Advanced Electromagnetics· 0 citations
Accurate forecasting of energy consumption in smart buildings is an important part of environmentally sustainable energy management and smart grid operations. Numerous studies have employed singular Machine Learning (ML) techniques to estimate a building's energy requirements; however, most reviews examine only a limited number of algorithms, forecasting horizons, or datasets. They do not consider how smart homes operate as a whole, how ensemble learning functions, or how to evaluate models. This paper provides a structured and systematic analysis of machine learning-based energy consumption forecasting methodologies within the extensive framework of smart home systems. This review differs from earlier surveys in that it examines (i) the architectural features of smart homes that affect data generation and forecasting accuracy, (ii) a wide range of supervised and ensemble regression methods, such as Random Forest (RF), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), Ridge, Lasso, voting regressors, and stacking regressors, and (iii) various evaluation and interpretability frameworks used to assess predictive reliability. The study meticulously assesses algorithms based on their precision, scalability, interpretability, computational cost, robustness to noise, and suitability for nonlinear energy patterns. This review also stresses the utility of hybrid and ensemble machine learning methods for improving prediction accuracy and system stability in changing home environments. The paper uses quantitative metrics, visualization tools, and Explainable Artificial Intelligence (XAI) methods from the literature to compare performance and examine forecasting outcomes. This work provides a comprehensive foundation for developing accurate, scalable, and comprehensible energy forecasting models for next-generation smart homes by integrating smart building system architecture, machine learning methodologies, ensemble techniques, and evaluation frameworks into a unified analytical perspective. The findings indicate unresolved issues and propose avenues for further investigation into hybrid modeling and real-time intelligent energy management systems.
Amin Namvari Gharehbolagh, A. Kalam, Yuan-Yuan Fan· Journal of Electronics and E...· 0 citations
Electricity is a vital resource that powers modern society, and reliable forecasting of electricity demand and supply is essential for the effective operation of power systems. Accurate forecasts allow power system operators to make informed decisions about generation, transmission, and distribution, which can help to prevent blackouts and other disruptions to the electricity supply. Various methods have been developed for forecasting electricity, including statistical methods such as ARIMA and Prophet and artificial intelligence (AI) algorithms such as recurrent neural network (RNN) and support vector machines (SVM). Recent advances in deep learning, particularly Long Short-Term Memory (LSTM) networks, have demonstrated superior performance for time-series forecasting tasks, especially with high-frequency datasets. In this paper, we compared the performance of six methods covering two statistical and four AI algorithms for forecasting electricity demand. They were applied to four different datasets: 1) A monthly KAPSARC, which stands for The King Abdullah Petroleum Studies and Research Center, Dataset in Saudi Arabia with limited historical data, 2) A generated hourly KAPSARC Dataset in Saudi Arabia, 3) An hourly PJM Dataset in USA with a large amount of data, and 4) A generated monthly PJM Dataset in USA. The performance of the approaches was different with each dataset. Overall, the results confirm that data richness particularly hourly granularity is a decisive factor in forecasting accuracy, and that deep learning models require substantial data volumes to outperform statistical baselines. These findings have direct implications for electricity infrastructure planning in Saudi Arabia under Vision 2030.
Kamal M. Othman· Journal of Intelligent Decis...· 0 citations
The growing adoption of artificial intelligence (AI) aims to improve global standards of living. However, this trend is accompanied by an increasing demand for energy. AI is rapidly integrating into home energy management systems (HEMS). While numerous artificial neural network (ANN) models have been developed for energy consumption forecasting, they often require significant computational resources and expertise for both development and deployment. This study presents a user-friendly engineering methodology for designing accurate multilayer perceptron (MLP) and Cascade-Forward Network (CFN) models to predict smart home energy consumption and solar generation. The approach emphasises computational simplicity and widespread practical viability. The dataset of 8399 hourly recorded weather and energy variables over the course of a year is extracted from publicly available data. It is randomly split into 70% for training, 15% for validation and 15% for testing. A proposed methodology guides the design, training, validation, and testing of various CFN-MLP models, in which weather variables with the greatest impact on energy generation and consumption are selected for model inputs based on different correlation tests. The identified optimal models each have two weather variables as inputs and two hidden layers of 8 and 16 neurons, balancing high predictive accuracy with low computational load for real-time HEMS deployment. The high prediction accuracy is supported by the small mean squared errors of 0.76 kWh and 0.0046 kWh. The models’ feasibility is facilitated by simple architectures, which demand only two smart sensors for the inputs, are affordable by most homes, and require small memory storage for the models.
D. Stoitseva-Delicheva, S. Yordanova· Applied Sciences· 0 citations
Accurate wind power forecasting is essential for the stable and economic operation of power systems with high renewable penetration. Although machine learning models have been widely adopted for this task, the assumption that greater model complexity invariably yields superior forecasting accuracy has received insufficient scrutiny. This paper presents a systematic experimental investigation that covers two complementary stages, i.e., wind speed correction and wind power forecasting. For wind speed correction, we compare 10 machine learning methods, including spanning linear, instance-based, and tree-based ensemble learners, under four newly proposed progressively enriched feature configurations. For wind power forecasting, we benchmark 20 methods spanning traditional machine learning, time-series deep learning, and Transformer-based architectures on two geographically distinct wind farms. Our results reveal a clear task-dependent pattern. In wind speed correction, tree-based ensemble methods, particularly gradient boosting variants, consistently dominate, and feature engineering contributes more to accuracy gains than model selection. In wind power forecasting, deep learning architectures substantially and consistently outperform traditional methods, with attention-based models generalizing the most robustly across regimes and recurrent networks proving to be the most sensitive to regime shifts. These findings provide actionable task-specific guidance for model selection in operational wind power forecasting systems.
