ELECTRICAL LOAD ANALYSIS TO AID DEMAND SIDE MANAGEMENT: THE CASE OF QATAR

Abstract

Demand side management (DSM) is an important strategy for promoting sustainable consumption in resource-rich countries with high purchasing power and subsidized tariffs. Global energy and environmental resource consumption have increased rapidly due to advances in production and transportation, leading to inefficient and wasteful use of resources. DSM aims to address these issues by promoting efficiency. For the implementation of appropriate DSM strategies, a greater understanding of consumption patterns is needed. To do so, load profiling and load clustering are two popular methods that can be used. Previous literature misses machine learning analysis of user demand to recommend policy objectives for electricity use in Qatar. This thesis aims to i) summarize the most recent global load profiling and clustering works, ii) use official smart meter data to understand key electricity consumption trends in Qatar, such as temperature-demand correlation, paying and non-paying, weekend vs. week-day, and public holiday consumption patterns, iii) perform load clustering to propose policies that would help manage the electric load in Qatar for its green growth, iv) assess the economic feasibility of rooftop photovoltaic (PV) systems for independent users, and v) recommend policy changes for Qatar in the light of the obtained results. The data at hand is spread among four sectors – i.e., commercial, government, hotels, and residential. It was found that among all the sectors there were only two usage periods of the same times. There is naturally a strong correlation between temperature and electricity consumption throughout the sectors. Furthermore, it was observed that the consumption of the sectors is highly similar which leads to multiple sectors being present in the same cluster. It was also noted that a rooftop PV system would be economically and energy-usage wise more beneficial to a higher consumption user. Finally, policy changes are proposed based on the results to encourage demand response programs in Qatar.

Date of Award	2023
Original language	American English
Awarding Institution	HBKU College of Science and Engineering