Application and Comparative Study of Time Series Analysis Algorithms in New Energy Forecasting

Smart Forecasting of Power Consumption with Machine Learning Models

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.

Evaluating and Comparing the Performance of Statistical and Machine Learning Algorithms to Achieve Successful Load Forecasting in Saudi Arabia

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.

International Journal of Statistics, Econometrics and Data Analysis Applications

Short-Term Electricity Demand Forecasting: A Comparative Evaluation of Models Based on Performance Criteria and Future Research Directions

Short-term electricity demand forecasting is a critical enabler of the secure and efficient operation of modern power systems, particularly amid increasing renewable energy integration, smart grid expansion, and the broader energy transition. This paper presents a rigorous comparative analysis of electricity demand forecasting models, encompassing statistical methods, Machine Learning (ML), Deep Learning (DL), and hybrid architectures. A structured taxonomy is proposed to classify models according to their methodological family, application horizon, and data requirements, thereby providing a unified reference framework for researchers and energy-sector practitioners. Models are evaluated using a multi-criteria framework comprising accuracy, robustness, scalability, interpretability, computational cost, and the capacity to handle exogenous variables. The analysis identifies critical research gaps, including the limited integration of probabilistic forecasting into operational contexts and the absence of standardized evaluation protocols under real-world conditions. Future research directions are outlined, with particular emphasis on uncertainty quantification, adaptive learning strategies, and hierarchical forecast coherence in systems with high penetration of distributed energy resources.

Electricity Spot Market Clearing Price Prediction Model: Algorithm Research and Application Based on Time Series Analysis

Against the backdrop of high-penetration renewable energy integration and increasingly intelligent power communication infrastructures, electricity spot market prices exhibit complex nonlinear fluctuations that directly affect the operational costs and scheduling efficiency of energy-intensive industrial systems. Accurate price forecasting is therefore essential for coordinated energy management and reliable information transmission in modern smart grids. This paper proposes a time-series prediction algorithm integrating fractal analysis and deep learning to capture both long-term memory characteristics and local abrupt variations in electricity price sequences. The method first quantifies long-range correlations using the Hurst exponent derived from rescaled range analysis, then extracts multi-scale fluctuation features through multifractal detrended fluctuation analysis, and finally embeds fractal characteristics into the loss function of a Long Short-Term Memory (LSTM) network to construct a fractal-aware prediction model. A dualwindow strategy is adopted to reconcile the statistical requirements of fractal estimation with temporal responsiveness. Experimental results based on actual clearing data from five domestic spot market pilot regions demonstrate that the proposed model consistently outperforms conventional ARIMA, standard LSTM, VMD-LSTM, CEEMDAN-BERT-LSTM, and Transformer-based methods in both MAPE and RMSE. The proposed framework provides an effective decisionsupport tool for intelligent energy management and offers potential reference value for signal-aware forecasting and information processing in advanced electromagnetic and communication-enabled power systems.

A Hybrid Ensemble Learning Strategy for Enhancing Solar Photovoltaic Forecasting in Renewable Energy Systems

The successful forecast of the solar photovoltaic (PV) power processing is essential in increasing grid stability, and in the ultimate inclusion of renewable energy. This paper is a research project aiming to introduce a hybrid classifier of Random Forest (RF), K-Nearest Neighbors (KNN), and Elastic Net (EN) to enhance multi-regional solar PV power forecasting using time-series. Preprocessing of Hourly PV generation data was done by taking out the temporal feature like hour, day and month to improve forecasting. To compare the proposed hybrid model with individual algorithms, the coefficient of determination (R2), mean absolute error (MAE) were used to evaluate the model. The results of the experiment indicate that the hybrid model had an R 2 of 0.91, which was higher than RF (0.88), KNN (0.82), and EN (0.79), and nearly halved the MAE, relative to single models. The results prove that the combination of multiple regression methods would lead to better prediction robustness, as well as lower variance and increase forecasting accuracy, which makes the suggested method an appropriate tool to study smart grids and sustainable energy planning.