Medium-term Energy Demand Analysis using Machine Learning: A Case Study on a Residential Apartment in a Divisional City of Bangladesh

Halima Haque, Md. Abdur Razzak
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Abstract

Being the innovation, machine learning has indisputably dominated energy consumption prediction from home to the industry all over the world. Many approaches have been acknowledged throughout the past decade to upgrade the energy demand which is not an alien to Bangladesh. Moreover, the energy consumption has been classified according to the convenience for both builders as well as end-users. As a consequence, such analysis has led to short, medium, and long-term consumption concept in forecasting of energy. This paper explores the energy demand analysis using conventional machine learning models for medium-term energy consumption. It also reviews the detail possibilities of energy consumption along with challenges. A forecast of power consumption based on a dataset of a residential apartment for a year has been presented with related parameters and results. Even an anticipated energy management plan has been discussed for Bangladesh.
利用机器学习进行中期能源需求分析:以孟加拉国某分区城市住宅公寓为例
作为一项创新,机器学习在全球范围内从家庭到行业的能耗预测中占据着无可争议的主导地位。在过去的十年中,许多方法已经得到承认,以提高能源需求,这对孟加拉国来说并不陌生。此外,根据对建筑商和最终用户的便利程度对能耗进行了分类。因此,这种分析导致了能源预测中的短期、中期和长期消费概念。本文探讨了利用传统的机器学习模型对中期能源消耗进行能源需求分析。它还详细回顾了能源消耗的可能性以及挑战。本文以某住宅楼为例,对某住宅楼一年的用电量进行了预测,并给出了相关参数和结果。孟加拉国甚至已经讨论了一项预期的能源管理计划。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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