1. London - Loughborough Centre for doctoral research in energy demand Central House 14 Upper Woburn Place London, WC1H 0NN www.lolo.ac.uk ANNUAL COLLOQUIUM 2011 The Danger of Data. Assessing the availability and quality of data for tertiary sector energy demand forecast models. Ed Sharp PhD candidate - LoLo CDT – UCL energy institute – ucesres@ucl.ac.uk Introduction Energy demand forecasting has been carried out since the oil crisis in the 1970’s and the subsequent realisation of the need to match supply to demand. The methodologies employed in these models have developed iteratively alongside the associated applications. Despite the resultant incorporation of a complex and varied number of methods models still remain data intensive and dependent on the availability and quality of input data to some extent. This poster represents the result of a request by EDF to identify and analyse datasets that would be beneficial to their model forecasting demand in the tertiary sector across Europe. Key drivers of demand which are used as inputs into these models were identified through EDF’s models and the literature, common causes of variation between sources of these variables were then explored. The results below represent the findings in the context of the UK, data availability is shown as a time series with the maximum range shown as the percentage of the mean value for selected years. Causes of Variation Between Data Sources Conclusions PhD Research - The spatiotemporal patterns of energy demand and supply in the UK Variable Data Sectoral classification: The predominant cause of variation between data sources is the lack of standard classification of the sector. Most widely used are The United Nation’s International Standard Industrial Classifications (ISIC) and Eurostat’s Nomenclature statistique des Activités économiques dans la Communité Européenne (NACE). However the former includes significant sub sectors omitted from the latter (agriculture, forestry and fishery). This results in the substantial divergence seen in the Energy Consumption, Employee numbers and GVA variables where those sources using the ISIC scheme provide significantly higher values than those using NACE. Other causes of variation between data sources include: differing methods of calculation in particular GDP where complicated techniques require expert knowledge to understand. Inconsistent methods of harmonisation for example changing population values used to create per capita GDP values. Variations in the precision with which data is stored, for example where population statistics are stored to the nearest person created an unjustified perception on the accuracy of the data. Semantic inconsistencies for example where energy consumption is not explicitly referred to as either primary or delivered. All of these issues would not be a problem if they were clearly stated which highlights the lack of quality metadata. The above sources of variation are exacerbated by a lack of data for certain variables, significantly floor space which is the key driver of energy demand in the non- domestic sector. All of these issues could be mitigated by some simple measures. These include the creation of a centralised repository for the data which could make the reason for divergence clear and transparent through the creation of high standards for metadata. Beyond this many variables would benefit from the introduction and use of mandatory classification schemes, a measure that would be significantly more costly and complicated to implement. The most important lesson of the research however is to utilise online sources critically and beware of the danger of data. Aims and Objectives: 1.Create a simulation model depicting UK energy demand into the future using a fine spatial and temporal scale. 2.Creation of demand and supply profiles at approximately 0.5 degrees spatial and 1 hour temporal resolution. 3.Model the influence of climate on these profiles using measured meteorology and widely used scenarios. 4.Analyse the difference between supply and demand at alterable spatial scales. First steps: 1.Assess and summarise current state of related research 2.Download, harmonise and consolidate existing data on variables affecting demand including but not limited to meteorological, socio-demographic, technological and physical. 3.Characterise demand over space and time in a recent historical year. 4.Develop simple models to depict demand based on the prior steps. 5.Iteratively advance these models in sophistication and robustness. 6.Repeat process for supply using existing models and structures. 7.Analyse difference between supply and demand at alterable spatial scales.