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Buildings are a main contributor to global energy consumption, with urban form significantly affecting their energy use. To quantify these effects, many studies relied on simulated data for limited buildings or empirical data at coarse temporal scales (monthly or yearly). However, few studies conducted large-scale empirical analyses of hourly consumption. In this paper, we empirically evaluate the impacts of urban form on energy consumption across multiple temporal scales (hourly, daily, monthly, and daily peak) for residential and commercial buildings in Santa Clara, CA. Using population density as a proxy for urban form, we built regression models and analyzed non-linear relationships and interaction effects. Results show that energy use intensity (EUI) varies non-linearly and significantly with population density, building size, and building use type. By increasing population density by one level , hourly EUI could drop by up to 32%, 36%, and 74% for single-family residential, multi-family residential, and commercial buildings respectively. The trends and magnitudes are consistent across temporal scales for the same building use type and population density level. These findings can inform decision-making regarding land use and urban planning for decarbonization goals and demand-side management programs.