Predicting Residential Energy Consumption Using Wavelet Decomposition With Deep Neural Network

Electricity consumption is accelerating due to economic and population growth. Hence, energy consumption prediction is becoming vital for overall consumption management and infrastructure planning. Recent advances in smart electric meter technology are making high-resolution energy consumption data available. However, many parameters influencing energy consumption are not typically monitored for residential buildings. Therefore, this study’s main objective is to develop a data-driven energy consumption forecasting model (next-hour consumption) for residential houses solely based on analyzing electricity consumption data. This research proposes a deep neural network architecture that combines stationary wavelet transform features and convolutional neural networks. The proposed approach utilizes automatically extracted features from smart-meter readings by applying wavelet decomposition, convolution, and pooling operations. This study’s findings have demonstrated the advantage of integrating wavelet features with convolutional neural networks to improve forecasting accuracy while automating feature extraction

An empirical study of the impacts of redevelopment project on property prices of residential buildings in Hong Kong

Thesis (B.Sc)--University of Hong Kong, 2009.Includes bibliographical references (p. 93-102).published_or_final_versio

A review on the contribution of crop diversification to Sustainable Development Goal 1 “No poverty” in different world regions

I am grateful to Professor Maggie Gill and Dr Leslie Lipper for initial discussions, to Dr Leslie Lipper for arranging the meetings with the experts at the Food and Agriculture Organization (FAO) and to the experts at FAO for the valuable discussions on the topic. I would also like to thank Kirsten MacSween for revising the English. This research has been funded by the UK Natural Environment Research Council (NERC), NE/N005619/1.Peer reviewedPublisher PD

Determinants of Livelihood Choices

The study identifies socio-economic determinants of livelihood choices of rural households in Bangladesh (4,195 households from 139 villages) by applying a multinomial logit model of occupational choice and a multivariate Tobit model that allows for jointness in decision making. Results reveal that households choose multiple livelihood options. A number of socio-economic factors and resources at the household level as well as the state of rural infrastructure significantly determine households’ livelihood choice. Overall, resource-rich and educated households engage in diversified livelihoods and rural infrastructure promotes such diversification. Female-headed households fail to participate in any of the livelihood categories and consequently earn significantly lower income. Policy implications include investment in rural infrastructure, irrigation, rural electrification, education, livestock resources, as well as targeting of female-headed households, for example, creation of a hired labour market and skills/education programmes for females

Hepatitis B Screening of At-Risk Immigrants Seen at Primary Care Clinics: A Quality Improvement Project

Objective: To test an intervention to increase screening for hepatitis B (HBV) in at-risk immigrants in the primary care setting. Patients and Methods: From a Mayo Clinic primary care panel, we identified approximately 19,000 immigrant patients from 9 high-risk countries/ethnic groups with intermediate or high prevalences of chronic HBV. Eligible patients with no record of prior HBV testing scheduled for primary care visits within the study period spanning October 1, 2017, through October 31, 2018, were identified. During the intervention period, the primary health care professional was notified by email 1 week prior to each primary care visit and encouraged to discuss screening for HBV infection and order screening tests at the appointment. We assessed rates of HBV screening during control and intervention periods. Results: We identified 597 patients in the control period and 212 patients in the intervention period who had not been screened previously for HBV. During the intervention period, 31.4% (58) of the 185 eligible patients were screened for HBV vs 7.2% (43) of the 597 eligible patients in the control period. Thus, the intervention resulted in a 4.3-fold increase in screening (P<.00001). Of the 101 patients screened in the at-risk population, 22 (21.8%) screened positive for prior exposure to HBV (hepatitis B core antibody–positive) and 6 (5.9%) for chronic HBV infection (hepatitis B surface antigen–positive). Conclusion: Notifying primary care physicians of the high-risk status of immigrant patients substantially increased screening for HBV. Identifying patients with HBV is important for monitoring disease prevalence, preventing transmission, and initiating treatment and cancer surveillance, allowing earlier recognition and prevention of chronic hepatitis, disease reactivation, cirrhosis, and hepatocellular carcinoma