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Gopal N Chitalia – Dual Degree CE

Gopal N Chitalia received his MS Dual Degree in Civil Engineering (CE). His research work was supervised by Prof. Vishal Garg . Here’s a summary of Gopal N Chitalia’s thesis Robust Short-term Electrical Load Forecasting Framework for Commercial Buildings using Deep Recurrent Neural Networks:

Building sector consumes nearly 75% of electricity sales. And with rapid population and economic expansion, electricity consumption in buildings are projected to be doubled by 2050. The building sector generates one-third of greenhouse gas, two-thirds of halo-carbon and about one-third of black-carbon emissions. This growing energy demands have raised significant concerns around the globe regarding it’s adverse effect on environment. The energy savings potential could reach as high as 30-80% with presently available building technologies. Therefore, importance on development of smart buildings, smart grids are being given which in turn can reduce carbon emissions. Accurate load forecasting is of utmost importance for the development of any modern power system. Hence, accurate building-level load forecasting can help deliver efficient building operations, thus mitigating such adverse effects. Load forecasting helps in real time building control of energy systems, including demand response, demand management, energy transactions, charging/discharging energy storage units. This work presents a robust short-term electrical load forecasting framework that can capture the variations in building operation, regardless of building type and location. Nine different hybrids of recurrent neural networks and clustering are explored. The test cases involve five commercial buildings of five different building types, i.e., academic, research laboratory, office, school and grocery store, located at five different locations in Bangkok-Thailand, Hyderabad-India, Virginia-USA, New York-USA, and Massachusetts-USA. Load forecasting results indicate that the deep learning algorithms implemented in this thesis deliver 20-45% improvement in load forecasting performance as compared to the current state-of-the-art results for both hour-ahead and 24-ahead load forecasting. With respect to sensitivity analysis, it is found that: (i) the use of hybrid deep learning algorithms can take as less as one month of data to deliver satisfactory hour-ahead load prediction, (ii) similar to the clustering technique, 15-minute resolution data, if available, delivers 30% improvement in hour-ahead load forecasting, and (iii) the formulated methods are found to be robust against weather forecasting errors. (iv) the proposed framework are found to be robust to seasonal changes. Lastly, the forecasting results across all five buildings validate the robustness of the proposed deep learning framework for the short-term building-level electrical load forecasting tasks.