International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 04 | Apr 2024
p-ISSN: 2395-0072
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ENERGY CONSUMPTION PROFILER Deepanshu Jaiswal1, Asifa 2, Ms. Anjali Patel 3 Student, Dept of CSE-AI, NIET Greater Noida, Uttar Pradesh, India, Student, Dept of CSE-AI, NIET Greater Noida, Uttar Pradesh, India, Assistant Professor, Dept Of CSE-AI, NIET Greater Noida, Uttar Pradesh, --------------------------------------------------------------------***--------------------------------------------------------------------climate prediction [3]. A live dashboard displays all Abstract - This research paper introduces the Energy machine parameters, including efficiency, facilitating maintenance tasks.
Consumption Profiler (ECP), a powerful tool for effective energy management in today's resource-constrained world. The ECP utilizes data analytics, machine learning, and realtime monitoring to optimize energy consumption patterns in residential, commercial, and industrial settings. By combining diverse energy data sources, including electricity, gas, and renewable sources, the ECP can identify energyintensive processes and inefficiencies with precision. With real-time monitoring capabilities and historical data analysis, the ECP provides actionable insights into energy consumption trends, enabling stakeholders to make informed decisions for sustainability initiatives and resource allocation.
The plywood factories can reduce their energy consumption and lower costs by optimizing their operating conditions with the help of a valuable tool called a profiler. It utilizes an encoder for acoustic features and a decoder network that incorporates an attention mechanism [4]. This tool not only aids in the optimization process but also helps factories comply with environmental regulations by reducing greenhouse gas emissions and pollutants.
2. LITERATURE REVIEW
Key
Words: Energy Usage Profiling, Energy Consumption Monitoring, Energy Consumption Analysis, Energy Consumption Data Collection, Energy Consumption Modelling, Smart Grid Data Analysis, Energy Consumption, Energy Management Systems, Data-driven Energy.
There is a growing interest in optimizing energy usage across different sectors, which is reflected in the literature on energy consumption profilers. Various methods are used, from data-driven approaches like machine learning to hardware solutions like smart meters [5]. These profilers are applied in diverse sectors, and recent studies have focused on advanced algorithms to help make energy conservation decisions [6]. The integration of renewables and smart grids has increased the importance of energy profilers in ensuring sustainability. This highlights the multidisciplinary nature of the field, emphasizing the need for continued research to address evolving challenges.
1.INTRODUCTION The plywood industry is a significant energy consumer, and a significant portion of this consumption is due to boilers. Inefficient boiler operation can result in a considerable waste of energy and money. Due to climate change concerns and the recent surge in energy costs, energy considerations in production planning are gaining importance [1].
Energy consumption profiling involves monitoring energy usage in various sectors using smart meters, IoT devices, and data analytics to aid efficiency, cost-effectiveness, and sustainability goals [7]. This review explores key findings and trends in energy consumption profiling.
An energy consumption profiler has been developed for boiler machines used in the plywood industry. The main objective of this profiler is to optimize the operating conditions that are inefficient. It collects information related to energy consumption, fuel type, load, and combustion efficiency to identify areas where energy can be saved. This information is then used by decisionmakers to choose the most suitable energy storage technologies. The collected data is analysed to ensure the stability and robustness of decision-making studies [2]. The boiler machine's energy, thermal or steam, is calculated using parameters from the machine itself.
Energy consumption profilers leverage the power of data analytics and machine learning to identify patterns and anomalies in energy usage. This helps in making informed decisions for energy optimization [8]. By integrating renewable energy and demand response strategies, sustainability is enhanced. Residential profilers offer personalized recommendations for reducing energy footprints, while industrial and commercial sectors focus on advanced control systems and predictive maintenance to improve efficiency.
Energy values are stored, and machine efficiency is computed automatically and stored in the database. This data could then be used to identify the specific areas where energy saving is possible. Based on findings various opportunities exist to improve the precision of indoor
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Although energy consumption profilers have promising benefits, there are several challenges that need to be addressed. These include concerns about data privacy, the
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