Indonesia Consumption: Final Consumption: LPG and RG data was reported at 339,311.000 TJ in 2017. This records an increase from the previous number of 278,146.000 TJ for 2016. Indonesia Consumption: Final Consumption: LPG and RG data is updated yearly, averaging 173,375.000 TJ from Dec 2006 (Median) to 2017, with 12 observations. The data reached an all-time high of 455,957.000 TJ in 2015 and a record low of 50,281.000 TJ in 2006. Indonesia Consumption: Final Consumption: LPG and RG data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Indonesia – Table ID.RBA004: Energy Statistics: Consumption.

Indonesia Consumption: Non Energy Uses: LPG and RG data was reported at 170.000 TJ in 2017. This records a decrease from the previous number of 19,507.000 TJ for 2016. Indonesia Consumption: Non Energy Uses: LPG and RG data is updated yearly, averaging 11,591.000 TJ from Dec 2006 (Median) to 2017, with 9 observations. The data reached an all-time high of 19,507.000 TJ in 2016 and a record low of 27.000 TJ in 2015. Indonesia Consumption: Non Energy Uses: LPG and RG data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Indonesia – Table ID.RBA004: Energy Statistics: Consumption.

Indonesia Consumption: Statistical Differences: LPG and RG data was reported at 163.000 TJ in 2017. This records a decrease from the previous number of 6,209.000 TJ for 2016. Indonesia Consumption: Statistical Differences: LPG and RG data is updated yearly, averaging 4,333.500 TJ from Dec 2006 (Median) to 2017, with 12 observations. The data reached an all-time high of 114,204.000 TJ in 2014 and a record low of -75,697.000 TJ in 2015. Indonesia Consumption: Statistical Differences: LPG and RG data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Indonesia – Table ID.RBA004: Energy Statistics: Consumption.

Electricity consumption benchmarks – Survey responses matched with household consumption data for 25 households \r \r The AER is required to update electricity consumption benchmarks (available on www.energymadeeasy.gov.au) at least every three years. The benchmarks were initially developed in 2011. The update of the benchmarks is currently being undertaken, and this is a small subset of the data. Once the study is finalised, the whole dataset will be made available via www.data.gov.au. \r \r This data is made up of two elements:\r \r 1.\tResponses to a survey from 25 Victorian householders about their energy consumption (shown in the tab ‘questionnaire responses’).\r \r 2.\tEnergy consumption data (in Watt Hours (WH)) for each household in the sample from 1 April 2012 to 31 March 2014 (or such time as data are available after the installation of a smart meter). E_0000_WH refers to WH usage in the half hour commencing 12am. The column TYPE shows the type of usage. There are three types – general, controlled load (where the household has a dedicated circuit for a specific appliance, such as hot water) and generation (where the household has solar panels, this shows the WH exported to the grid from the solar panels - note that electricity generated and used within the house is not measured). \r \r There is also a word document titled ‘questionnaire’, which shows the survey questions. The corresponding question number in the spreadsheet shows the data for that question. \r

This data set is provided in support of a forthcoming paper: "Impact of uncoordinated plug-in electric vehicle charging on residential power demand," [1]. These files include electricity demand profiles for 200 households randomly selected among the ones available in the 2009 RECS data set for the Midwest region of the United States. The profiles have been generated using the modeling proposed by Muratori et al. [2], [3], that produces realistic patterns of residential power consumption, validated using metered data, with a resolution of 10 minutes. Households vary in size and number of occupants and the profiles represent total electricity use, in watts. The files also include in-home plug-in electric vehicle recharging profiles for 348 vehicles associated with the 200 households assuming both Level 1 (1920 W) and Level 2 (6600 W) residential charging infrastructure. The vehicle recharging profiles have been generated using the modeling proposed by Muratori et al. [4], that produces real-world recharging demand profiles, with a resolution of 10 minutes. [1] M. Muratori, "Impact of uncoordinated plug-in electric vehicle charging on residential power demand." Forthcoming. [2] M. Muratori, M. C. Roberts, R. Sioshansi, V. Marano, and G. Rizzoni, "A highly resolved modeling technique to simulate residential power demand," Applied Energy, vol. 107, no. 0, pp. 465 - 473, 2013. https://doi.org/10.1016/j.apenergy.2013.02.057 [3] M. Muratori, V. Marano, R. Sioshansi, and G. Rizzoni, "Energy consumption of residential HVAC systems: a simple physically-based model," in 2012 IEEE Power and Energy Society General Meeting. San Diego, CA, USA: IEEE, 22-26 July 2012. https//doi.org/10.1109/PESGM.2012.6344950 [4] M. Muratori, M. J. Moran, E. Serra, and G. Rizzoni, "Highly-resolved modeling of personal transportation energy consumption in the United States," Energy, vol. 58, no. 0, pp. 168-177, 2013. https://doi.org/10.1016/j.energy.2013.02.055

Indonesia Consumption: Total Energy Requirements: LPG and RG data was reported at 246,996.000 TJ in 2017. This records an increase from the previous number of 199,010.000 TJ for 2016. Indonesia Consumption: Total Energy Requirements: LPG and RG data is updated yearly, averaging 116,842.500 TJ from Dec 2006 (Median) to 2017, with 12 observations. The data reached an all-time high of 274,082.000 TJ in 2015 and a record low of 16,134.000 TJ in 2006. Indonesia Consumption: Total Energy Requirements: LPG and RG data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Indonesia – Table ID.RBA004: Energy Statistics: Consumption.

The Hydreon RG-15 Solid State Tipping Bucket is a rainfall measuring device intended to replace conventional tipping buckets. The RG-15 is rugged, reliable, maintenance-free and features a nominal accuracy of within 10%. The RG-15 is designed to replace tipping bucket rain gauges in many applications where their maintenance requirements make them impractical. The RG-15 uses beams of infrared light within a plastic lens about the size of a tennis ball. The round surface of the lens discourages collection of debris, and the RG-15 has no moving parts to stick, and no water-pathways to clog. The device features an open-collector output that emulates a conventional tipping bucket, as well as serial communications that provide more detailed data and allow for configuration of the device. The RG-15 may be configured through the serial port, or optionally via DIP switches. Power consumption of the RG-15 is very low, and the device is well-suited to solar-power applications. Dip Switches can control the units (inches or millimeters) and resolution (0.01″/0.2mm or 0.001″/0.02mm) of the device. Commands can also be sent via the RS232 serial port to override them.

The Republic of the Congo: Natural gas consumption: The latest value from 2023 is 0.016 quadrillion Btu, unchanged from 0.016 quadrillion Btu in 2022. In comparison, the world average is 0.788 quadrillion Btu, based on data from 189 countries. Historically, the average for the Republic of the Congo from 1980 to 2023 is 0.013 quadrillion Btu. The minimum value, 0 quadrillion Btu, was reached in 1980 while the maximum of 0.061 quadrillion Btu was recorded in 2013.

BackgroundAlcohol consumption behaviors and alcohol use disorder risk and presentation differ by sex, and are associated with blood concentrations of the steroid sex hormones, testosterone and estradiol, and their regulatory binding proteins, sex hormone binding globulin (SHBG) and albumin. Genetic variation is also associated with alcohol consumption, alcohol use disorder, and levels of these hormones and binding proteins.MethodsTo assess the contribution of genetic factors to previously described phenotypic associations between alcohol-use traits and sex-hormone levels, we estimated genetic correlations (rg) using summary statistics from prior published, large sample size genome-wide association studies (GWAS) of alcohol consumption, alcohol dependence, testosterone, estradiol, SHBG, and albumin. We defined statistical significance at p < 0.005 and trends at p < 0.05.ResultsFor alcohol consumption, we observed positive genetic correlation (i.e. genetic effects in the same direction) with SHBG in females (rg = 0.089, p = 0.004) and a trend toward negative genetic correlation (i.e. genetic effects in opposite directions) with bioavailable testosterone (rg = -0.064, p = 0.032); however there were only trends toward positive genetic correlation with total testosterone in males (rg = 0.084, p = 0.007) and with albumin in a sex-combined cohort (rg = 0.082, p = 0.015). For alcohol dependence, we observed trends toward negative genetic correlation with total testosterone in females (rg = -0.106, p = 0.024) and positive genetic correlation with BMI-adjusted SHBG in males (rg = 0.119, p = 0.017). Some of these genetic correlations were different than the corresponding phenotypic associations, and some may suggest differences between females and males.ConclusionsShared genetic effects might contribute to positive associations of alcohol consumption with albumin and between alcohol dependence and SHBG in males; however, most of the phenotypic associations between alcohol-use traits and levels of sex hormones and their binding proteins did not correspond to broadly shared genetic effects in the same direction. Some even corresponded to genetic effects in the opposite direction. Future studies of these traits should include GWAS on larger cohorts by sex and investigation of localized correlations of genetic effects and the relative contributions of heritable and environmental factors.

Indonesia Consumption: Households and Other Consumers: Other Consumers: LPG and RG data was reported at 9,103.000 TJ in 2017. This records a decrease from the previous number of 38,971.000 TJ for 2016. Indonesia Consumption: Households and Other Consumers: Other Consumers: LPG and RG data is updated yearly, averaging 6,886.000 TJ from Dec 2006 (Median) to 2017, with 9 observations. The data reached an all-time high of 38,971.000 TJ in 2016 and a record low of 5,465.000 TJ in 2008. Indonesia Consumption: Households and Other Consumers: Other Consumers: LPG and RG data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Indonesia – Table ID.RBA004: Energy Statistics: Consumption.

The Republic of the Congo: Primary energy consumption: The latest value from 2023 is 0.045 quadrillion Btu, unchanged from 0.045 quadrillion Btu in 2022. In comparison, the world average is 3.088 quadrillion Btu, based on data from 190 countries. Historically, the average for the Republic of the Congo from 1980 to 2023 is 0.033 quadrillion Btu. The minimum value, 0.01 quadrillion Btu, was reached in 1983 while the maximum of 0.099 quadrillion Btu was recorded in 2013.

Coffee or caffeine consumption has been associated with neuropsychiatric disorders, implying a shared etiology. However, whether these associations reflect causality remains largely unknown. To understand the genetic structure of the association between decaffeinated coffee consumption (DCC) and neuropsychiatric traits, we examined the genetic correlation, causality, and shared genetic structure between DCC and neuropsychiatric traits using linkage disequilibrium score regression, bidirectional Mendelian randomization (MR), and genome-wide cross-trait meta-analysis in large GWAS Consortia for coffee consumption (N = 329,671) and 13 neuropsychiatric traits (sample size ranges from 36,052 to 500,199). We found strong positive genetic correlations between DCC and lifetime cannabis use (LCU; Rg = 0.48, P = 8.40 × 10−19), alcohol use disorder identification test (AUDIT) total score (AUDIT_T; Rg = 0.40, P = 4.63 × 10−13), AUDIT_C score (alcohol consumption component of the AUDIT; Rg = 0.40, P = 5.26 × 10−11), AUDIT_P score (dependence and hazardous-use component of the AUDIT; Rg = 0.28, P = 1.36 × 10−05), and strong negative genetic correlations between DCC and neuroticism (Rg = −0.15, P = 7.27 × 10−05), major depressed diseases (MDD; Rg = −0.15, P = 0.0010), and insomnia (Rg= −0.15, P = 0.0007). In the cross-trait meta-analysis, we identified 6, 5, 1, 1, 2, 31, and 27 shared loci between DCC and Insomnia, LCU, AUDIT_T, AUDIT_C, AUDIT_P, neuroticism, and MDD, respectively, which were mainly enriched in bone marrow, lymph node, cervix, uterine, lung, and thyroid gland tissues, T cell receptor signaling pathway, antigen receptor-mediated signaling pathway, and epigenetic pathways. A large of TWAS-significant associations were identified in tissues that are part of the nervous system, digestive system, and exo-/endocrine system. Our findings further indicated a causal influence of liability to DCC on LCU and low risk of MDD (odds ratio: 0.90, P = 9.06 × 10−5 and 1.27, P = 7.63 × 10−4 respectively). We also observed that AUDIT_T and AUDIT_C were causally related to DCC (odds ratio: 1.83 per 1-SD increase in AUDIT_T, P = 1.67 × 10−05, 1.80 per 1-SD increase in AUDIT_C, P = 5.09 × 10−04). Meanwhile, insomnia and MDD had a causal negative influence on DCC (OR: 0.91, 95% CI: 0.86–0.95, P = 1.51 × 10−04 for Insomnia; OR: 0.93, 95% CI: 0.89–0.99, P = 6.02 × 10−04 for MDD). These findings provided evidence for the shared genetic basis and causality between DCC and neuropsychiatric diseases, and advance our understanding of the shared genetic mechanisms underlying their associations, as well as assisting with making recommendations for clinical works or health education.

Indonesia Consumption: Households and Other Consumers: Agriculture: LPG and RG data was reported at 22.000 TJ in 2017. This records an increase from the previous number of 21.000 TJ for 2016. Indonesia Consumption: Households and Other Consumers: Agriculture: LPG and RG data is updated yearly, averaging 17.000 TJ from Dec 2006 (Median) to 2017, with 12 observations. The data reached an all-time high of 22.000 TJ in 2017 and a record low of 7.000 TJ in 2006. Indonesia Consumption: Households and Other Consumers: Agriculture: LPG and RG data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Indonesia – Table ID.RBA004: Energy Statistics: Consumption.

This dataset should be read alongside other energy consumption datasets on the City of Melbourne open data platform as well as the following report:\r
\r
http://imap.vic.gov.au/uploads/Meeting%20Agendas/2014%20August/Att%207a_IMAP_Energy_Map_-_CSIRO_-_Energy_Use_2011-2026_Report_-_2014June30_-Final_pdf_11.2MB.pdf\r
\r
The dataset outlines modelled energy consumption across the City of Melbourne municipality. It is not energy consumption data captured by a meter, but modelled data based on building attributes such as building age, floor area etc. This data was provided by the CSIRO as a result of a study commissioned by IMAP Councils. The study was governed by a Grant Agreement between Councils and the CSIRO, which stated an intent for the data to be published. This specific dataset is presented at a block level scale. It includes both commercial and residential buildings and is a 2011 baseline. It does not include the industrial sector.

This dataset is from the report Operational water consumption and withdrawal factors for electricity generating technologies: a review of existing literature (J. Macknick, R. Newmark, G. Heath and K.C. Hallett) and provides estimates of operational water withdrawal and water consumption factors for electricity generating technologies in the United States. Estimates of water factors were collected from published primary literature and were not modified except for unit conversions. The water factors presented may be useful in modeling and policy analyses where reliable power plant level data are not available.

This dataset should be read alongside other energy consumption datasets on the City of Melbourne open data platform as well as the following report:\r
\r
http://imap.vic.gov.au/uploads/Meeting%20Agendas/2014%20August/Att%207a_IMAP_Energy_Map_-_CSIRO_-_Energy_Use_2011-2026_Report_-_2014June30_-Final_pdf_11.2MB.pdf\r
\r
The dataset outlines modelled energy consumption across the City of Melbourne municipality. It is not energy consumption data captured by a meter, but modelled data based on building attributes such as building age, floor area etc. This data was provided by the CSIRO as a result of a study commissioned by IMAP Councils. The study was governed by a Grant Agreement between Councils and the CSIRO, which stated an intent for the data to be published. This specific dataset is presented at a block level scale. It includes both commercial and residential buildings and is a 2016 business-as-usual projection, relative to a 2011 baseline. It does not include the industrial sector.

The Republic of the Congo: Gasoline consumption, thousand barrels per day: The latest value from 2023 is 3.73 thousand barrels per day, an increase from 3.62 thousand barrels per day in 2022. In comparison, the world average is 139.36 thousand barrels per day, based on data from 190 countries. Historically, the average for the Republic of the Congo from 1980 to 2023 is 2.7 thousand barrels per day. The minimum value, 0.96 thousand barrels per day, was reached in 2000 while the maximum of 4.5 thousand barrels per day was recorded in 1990.

The Republic of the Congo: Electricity consumption percent of world total: The latest value from 2023 is 0.011 percent, an increase from 0.01 percent in 2022. In comparison, the world average is 0.529 percent, based on data from 189 countries. Historically, the average for the Republic of the Congo from 1980 to 2023 is 0.005 percent. The minimum value, 0.002 percent, was reached in 2000 while the maximum of 0.011 percent was recorded in 2023.

The Republic of the Congo: Liquefied petroleum gas consumption, thousand barrels per day: The latest value from 2023 is 0.29 thousand barrels per day, unchanged from 0.29 thousand barrels per day in 2022. In comparison, the world average is 57.49 thousand barrels per day, based on data from 190 countries. Historically, the average for the Republic of the Congo from 1980 to 2023 is 0.2 thousand barrels per day. The minimum value, 0.04 thousand barrels per day, was reached in 2000 while the maximum of 0.37 thousand barrels per day was recorded in 2009.