Forecasting the Performance of US Stock Market Indices During COVID-19: RF vs LSTM

The US stock market experienced instability following the recession (2007-2009). COVID-19 poses a significant challenge to US stock traders and investors. Traders and investors should keep up with the stock market. This is to mitigate risks and improve profits by using forecasting models that account for the effects of the pandemic. With consideration of the COVID-19 pandemic after the recession, two machine learning models, including Random Forest and LSTM are used to forecast two major US stock market indices. Data on historical prices after the big recession is used for developing machine learning models and forecasting index returns. To evaluate the model performance during training, cross-validation is used. Additionally, hyperparameter optimizing, regularization, such as dropouts and weight decays, and preprocessing improve the performances of Machine Learning techniques. Using high-accuracy machine learning techniques, traders and investors can forecast stock market behavior, stay ahead of their competition, and improve profitability. Keywords: COVID-19, LSTM, S&P500, Random Forest, Russell 2000, Forecasting, Machine Learning, Time Series JEL Code: C6, C8, G4.Comment: Pennsylvania Economic Association (PEA)- June 202

Acoustic-Based Online Monitoring of Cooling Fan Malfunction in Air-Forced Transformers Using Learning Techniques

Cooling fans are one of the critical components of air-forced dry-type transformers for regulating internal temperatures. Therefore, effective malfunction detection is crucial to maintain the transformer temperature within an acceptable range and prevent overheating. Current malfunction detection of cooling fans in certain types of transformers relies on complementary indicators, such as top-oil temperature, oil convection, dissolved gas, and oil quality. However, these conventional indicators are not directly applicable to air-forced transformers, which primarily use cooling fans as their cooling system. To overcome this challenge, this study utilizes cooling fan audio records as indicators. The audio signals are classified into normal and malfunctioning classes using advanced learning algorithms, including convolutional neural networks and random forests. Learning algorithms require transforming recorded audio data into proper formats. Accordingly, convolutional neural networks are trained based on spectrogram images derived from audio signals. For random forests, various time-frequency feature extraction methods are used to derive meaningful representations from audio signals. Besides, multiple data augmentation techniques are employed to enhance the dataset size and diversity. Algorithmic performance is optimized through hyperparameter tuning and classifier threshold adjustment. To further validate the model, a test is conducted on another dataset to evaluate the fitted learning model applicability in real-world applications. Simulations reveal that convolutional neural networks outperform random forests, whereas the latter provides superior interpretability of acoustic features compared to the former

Optimization of Residential Demand Response Program Cost with Consideration for Occupants Thermal Comfort and Privacy

Residential consumers can use the demand response program (DRP) if they can utilize the home energy management system (HEMS), which reduces consumer costs by automatically adjusting air conditioning (AC) setpoints and shifting some appliances to off-peak hours. If HEMS knows occupancy status, consumers can gain more economic benefits and thermal comfort. However, for the building occupancy status, direct sensing is costly, inaccurate, and intrusive for residents. So, forecasting algorithms could serve as an effective alternative. The goal of this study is to present a non-intrusive, accurate, and cost-effective approach, to develop a multi-objective simulation model for the application of DRPs in a smart residential house, where (a) electrical load demand reduction, (b) adjustment in thermal comfort (AC) temperature setpoints, and (c) , worst cases scenario approach is very conservative. Because that is unlikely all uncertain parameters take their worst values at all times. So, the flexible robust counterpart optimization along with uncertainty budgets is developed to consider uncertainty realistically. Simulated results indicate that considering uncertainty increases the costs by 36 percent and decreases the AC temperature setpoints. Besides, using DRPs reduces demand by shifting some appliance operations to off-peak hours and lowers costs by 13.2 percent