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23 articles for “Electricity Demand Forecasting”
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LightGBM-Based Grid Demand Estimation and Prediction for Intelligent Electricity Load Forecasting
Abstract: Correctly predicting electricity demand in emerging energy markets like India is the most effective way to maintain grid stability. This is particularly important. This article offers a comprehensive data- based structure that captures both the broad temporal dynamics and regional diversity. This technique utilizes a large-scale multi-dimensional feature space that includes temporal indicators, autoregressive lag terms, sliding-window rolling statistics, and state-level administrative markers. Our systematic benchmarking approach involves the use …
Published in Journal of Power Electronics and Power Systems · Vol. 16, Issue 3, 2026 Read article
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Rapid Forecasting of Short-run Electric Power Demand Profiles of India Using the Statistical Method of Z Scores
Abstract: Power demand profile prediction for a region or nation is a critical part of the energy system design and operational planning process. A simplified method based on non-dimensionalizing power demand data from previous years using Z scores calculated from the mean and standard deviation of the profiles is developed in this study to forecast monthly demand profiles at time resolution of 1 hour for future years. The Z score range …
Published in Research & Reviews : Journal of Statistics · Vol. 14, Issue 1, 2025 · pp. 38–48 Read article
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Time Series Forecasting of Electricity Consumption: A Comparative Analysis of ARIMA and SARIMA Models
Abstract: Accurate electricity demand forecasting plays a vital role in energy planning, efficient power system operation, and sustainable resource management. This study conducts a comparative evaluation of the Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA) models using ten years of monthly electricity consumption data collected from a national electricity regulatory authority. The performance of both models is assessed using forecasting accuracy metrics, including Mean Absolute Error …
Published in Research & Reviews : Journal of Statistics · Vol. 15, Issue 2, 2026 · pp. 43–53 Read article
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Support Vector Machine Inspired Load Forecasting of a State University in Haryana
Abstract: Estimating the possible environmental impact and determining probable capital requirements are made easier with a solid grasp of electricity demand. Beginning in the middle of the 20th century, demand forecasting for electric power networks was studied theoretically. Prior to that, the study of demand forecasting had not developed because of the small scale of power networks. With the use of statistical prediction techniques, plans for the electric power industry have …
Published in Trends in Electrical Engineering · Vol. 15, Issue 2, 2025 · pp. 33–40 Read article
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Data-Driven Energy Forecasting for Smart Homes: Ensemble Learning from IoT Meters and Relevance for Polymer-Composite Based Smart Infrastructure
Abstract: Reliable estimation of household electricity demand is relevant in creating efficiency in energy usage, optimization of the loads, and intelligent demand-side management in intelligent grid systems. This paper introduces a varied machine learning model that approaches residential electric consumption prediction using an assortment of ensemble regression boosts, including Linear Regression, Lasso Regression, Decision Tree Regressor, Random Forest, and Gradient Boosting, to predict residential electricity consumption environments on a time-series arrested …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 29–64 Read article
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Short-Term Load Demand Forecasting using Chaos Theory and ANFIS
Abstract: In the electrical power sector, forecasting of load demand is an important process for effective planning of future expansion and periodical operations including unit commitments, fuel scheduling, short-term maintenance, security assessments, reducing spinning reserve, reliability analysis etc. Accurate load predictions are also necessary to utilize the electrical energy efficiently and to minimize the conflicts between the demand and supply of electricity. As electric load pattern of a region is very …
Published in Trends in Electrical Engineering · Vol. 6, Issue 2, 2016 · pp. 50–57 Read article
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Controlled Temporal Feature Ablation and Explainable Ensemble Learning for Next- Hour Residential Electricity Forecasting
Abstract: Short-term residential load forecasting is increasingly used in demand response, home energy management, and data-driven grid operation, yet reported gains are often difficult to attribute to either the forecasting algorithm or the temporal representation supplied to it. This study presents a controlled, leakage-aware evaluation of temporal feature engineering for one-hour-ahead household electricity forecasting. The public UCI Individual Household Electric Power Consumption dataset was aggregated to hourly resolution, causally imputed using …
Published in Trends in Electrical Engineering · Vol. 13, Issue 3, 2023 Read article
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Safar – Sathi: A Smart Ride-Sharing Platform
Abstract: In the midst of the increased use of the internet and technology‚ people have begun to change their transportation habits․ This is why we propose Safar-Sathi: A Smart Ride-Sharing Platform‚ a smartphone-based ride-sharing platform that provides cheap‚ convenient and secure transportation services by connecting passengers and nearby drivers․ The application can be used for user registration‚ ride booking‚ real-time tracking on GPS‚ fare calculation‚ user profile building‚ ride history‚ and …
Published in International Journal of Mobile Computing Technology · Vol. 4, Issue 2, 2026 Read article
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Green Q-Commerce: Balancing Speed and Sustainability in Hyper-Fast Delivery Systems
Abstract: The rapid expansion of quick commerce (q-commerce) has transformed consumer expectations with ultra-fast deliveries, yet its environmental impact—marked by carbon-intensive logistics, excessive packaging waste, and energy-heavy operations—poses significant sustainability challenges. This review examines how the q-commerce sector can reconcile speed with ecological responsibility through innovative solutions, including electrified last-mile delivery (e-bikes, electric vehicles, drones), circular packaging models (reusable containers, biodegradable materials), and artificial intelligence (AI)-driven logistics optimization (route efficiency, demand …
Published in E-Commerce for Future & Trends · Vol. 12, Issue 2, 2025 · pp. 22–27 Read article
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Defining Regression Polynomials with Process Similarity Criteria
Abstract: Highly accurate monitoring and prediction is essential in detecting abnormality events and forecasting demands in supply chains. This investigation provides a computational procedure for defining polynomials that satisfy similarity criteria in the monitoring process for a polynomial of any degree. It provides an example of using algorithms to predict transient thermal processes in electronic devices. The second-degree polynomial defined with this algorithm is compared with a polynomial defined using a …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 10, Issue 2, 2023 · pp. 13–20 Read article
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Use of Machine Learning Algorithms in Power System Load Forecasting: A review
Abstract: The electrical load forecasting has become one of the most important fields of research for secured, efficient, accurate, reliable power dispatch and management. The main aim of load demand forecasting is to predict the load demand for accurate generation scheduling, system security and economic dispatch of load at any time. Many Authors have done research on the evaluation of load forecasting methods, to improve the accuracy of prediction. In load …
Published in International Journal of Microwave Engineering and Technology Read article
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Real-Time Air Quality Prediction Using IoT-Integrated Polymer Sensors and Recurrent Neural Networks
Abstract: Real-time air quality monitoring remains a critical challenge in urban environments, where traditional sensor infrastructures often suffer from limited responsiveness, poor scalability, and high deployment costs. The increasing prevalence of NO₂ pollution, a key contributor to respiratory and cardiovascular ailments, demands advanced sensing platforms capable of both accurate detection and predictive inference. Existing methods either rely on rigid electronic sensors lacking adaptability or on statistical forecasting models that fail to …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 332–347 Read article
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Home Appliances Automation System
Abstract: Abstract: The past decade has been very interesting if we talk about power system. The increasing demand of electricity has led to a rapid increase in electricity production. India is a country with a vast population and to meet the demand of such a vast population requires a power planning, load forecasting, sustainably developed resources, energy saving plans, and with all such planning, smart solutions to save resources. Hence, in …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 6, Issue 3, 2019 · pp. 14–19 Read article
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DATABASE-DRIVEN ENERGY MANAGEMENT IN ELECTRIC VEHICLES
Abstract: With the growing concern over environmental pollution, there is an increasing demand for sustainable and eco-friendly technologies. Among these, electric vehicles (EVs) have emerged as a promising alternative to conventional fossil-fuel-based transportation. However, as EV adoption accelerates, efficient energy management becomes critical to enhance vehicle performance, extend battery life, and ensure overall system reliability. This research presents a Database-Driven Energy Management System (DBEMS) that leverages real-time data from EV components …
Published in Journal of Automobile Engineering and Applications · Vol. 12, Issue 3, 2025 · pp. 19–24 Read article
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Autonomous Delivery Drone or Vehicle Fleet Managerial System
Abstract: This review presents a clear conceptual framework for an autonomous delivery drone / vehicle fleet managerial system, emphasizing Artificial Intelligence (AI), Machine Learning (ML), Electrical and Electronics Engineering, and operational safety. The proposed architecture receives delivery requests, checks payload and route feasibility, assigns a suitable drone or ground vehicle, predicts travel time and energy demand, monitors missions through telemetry, and re-plans when traffic, weather, battery state, or vehicle availability changes. …
Published in Journal of Control & Instrumentation · Vol. 17, Issue 3, 2026 Read article
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Strategies for Efficient Integration of Distributed Energy Resources into Microgrid Systems
Abstract: With the growing integration of Distributed Energy Resources into modern power systems, the global energy landscape is changing. Some of the DERs are solar photovoltaic (PV), wind turbines, battery storage systems, combined heat and power (CHP) units, and electric vehicles (EVs). Some of the advantages include lower transmission losses, better energy efficiency, and more resilience to grid failures. However, the far-reaching integration of DERs carries with it considerable technical, economic, …
Published in Journal of Power Electronics and Power Systems · Vol. 15, Issue 3, 2025 · pp. 51–56 Read article
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Smart Grid Energy Saving Technique Using Machine Learning
Abstract: The energy authorities provided the computation at reasonable rates for straightforward congestion management. By giving the specific information needed for energy use that is aimed towards client demands, the microgrid enables significant energy cost reductions & better power cash reserves. Processing data and producing predictions for a developing electrical system would be difficult, nevertheless, given the analysis that has been done and the population's characteristics. A Deep Learning (DL) Electricity …
Published in Journal of Instrumentation Technology & Innovations · Vol. 12, Issue 3, 2022 · pp. 1–10 Read article
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Strategic Approaches to Sustainable Lithium Extraction: Advances in Technology, Resource Recovery, and Environmental Management
Abstract: Lithium, a critical mineral for modern technological advancements, is in high demand due to its role in the production of lithium-ion batteries, which power electric vehicles, portable electronics, and renewable energy storage solutions. This paper provides a comprehensive overview of lithium’s sources, including pegmatites, brine deposits, and sedimentary lithium minerals, while examining production, extraction methodologies, and challenges related to sustainability and environmental impact. Additionally, advancements in laser-induced breakdown spectroscopy (LIBS) …
Published in International Journal of Minerals · Vol. 1, Issue 2, 2024 · pp. 8–13 Read article
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AI-BASED SMART RESOURCE DEMANDAND PRICE PREDICTION USING MACHINE LEARNING FOR EFFICIENT RESOURCE MANAGEMENT
Abstract: AI and machine learning models now help manage resource supplies and price changes across key Indian markets. The development and application of advanced predictive analytics in India's agriculture supply chain and wholesale energy market systems are examined here. Although agriculture occupies over 40% of India's land area, it accounts for approximately 15–18% of the national Gross Domestic Product (GDP). In order to regulate the PDS, unify land information, and optimize …
Published in Trends in Electrical Engineering · Vol. 16, Issue 3, 2025 Read article
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Advanced Energy Management in Distribution Substations Incorporating Energy Storage and Demand Side Management for Increased Profitability
Abstract: In recent years, energy system going very complex because of more renewable sources, electric vehicles, storage devices and smart consumer demand. Old distribution grid cannot handle this complexity properly. For this reason, many researchers work on new energy management systems, home energy control, distributed control, and advanced distribution management. Many papers study about home energy management for flexible use of energy, predictive grid operation, and demand response. Some papers give …
Published in Trends in Electrical Engineering · Vol. 16, Issue 3, 2025 Read article