Electrical load forecasting models: a critical systematic review

Electricity forecasting is an essential component of smart grid, which has attracted increasing academic interest. Forecasting enables informed and efficient responses for electricity demand. However, various forecasting models exist making it difficult for inexperienced researchers to make an informed model selection. This paper presents a systematic review of forecasting models with the main purpose of identifying which model is best suited for a particular case or scenario. Over 113 different case studies reported across 41 academic papers have been used for the comparison. The timeframe, inputs, outputs, scale, data sample size, error type and value have been taken into account as criteria for the comparison. The review reveals that despite the relative simplicity of all reviewed models, the regression and/or multiple regression are still widely used and efficient for long and very long-term prediction. For short and very short-term prediction, machine-learning algorithms such as artificial neural networks, support vector machines, and time series analysis (including Autoregressive Integrated Moving Average (ARIMA) and the Autoregressive Moving Average (ARMA)) are favoured. The most widely employed independent variables are the building and occupancy characteristics and environmental data, especially in the machine learning models. In many cases, time series analysis and regressions rely on electricity historical data only, without the introduction of exogenous variables. Overall, if the singularity of the different cases made the comparison difficult, some trends are clearly identifiable. Considering the large amount of use cases studied, the meta-analysis of the references led to the identification of best practices within the expert community in relation to forecasting use for electricity consumption and power load prediction. Therefore, from the findings of the meta-analysis, a taxonomy has been defined in order to help researchers make an informed decision and choose the right model for their problem (long or short term, low or high resolution, building to country level)

Short Term Electricity Forecasting Using Individual Smart Meter Data

AbstractSmart metering is a quite new topic that has grown in importance all over the world and it appears to be a remedy for rising prices of electricity. Forecasting electricity usage is an important task to provide intelligence to the smart gird. Accurate forecasting will enable a utility provider to plan the resources and also to take control actions to balance the electricity supply and demand. The customers will benefit from metering solutions through greater understanding of their own energy consumption and future projections, allowing them to better manage costs of their usage. In this proof of concept paper, our contribution is the proposal for accurate short term electricity load forecasting for 24hours ahead, not on the aggregate but on the individual household level

The Impact of Center City Economic and Cultural Vibrancy on Greenhouse Gas Emissions from Transportation, Research Report 11-13

Urban planners and scholars have focused a great deal of attention on understanding the relationship between the built environment and transportation behavior. However, other aspects of the urban environment – including the vibrancy and quality of life in urban areas – have received little attention. This report seeks to close this gap by analyzing the effects of both land-use and urban vibrancy on transportation patterns. Analysis of data from a variety of sources suggests that in addition to the built-environment, the vibrancy of the urban environment also affects transportation behavior. Moreover, vibrancy affects land-use patterns. By integrating objective measures of center-city quality of life into transportation choice models, our new statistical results inform public policy. We discuss specific public policy options for reducing greenhouse gas emissions and increasing public transit use

Identification of Risk Factors Associated with Obesity and Overweight - A Machine Learning Overview

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Less Automation More Information: A Learning Tool for a Post-occupancy Operation and Evaluation

Climate change and the pandemic generated an urgent need to have an effi-cient urban habitat that includes technological innovations to deal with the ecological and digital transitions. Italy counts about 14 million buildings, 12 of which are houses, responsible for more than 40% of final energy consumption, most of which is ascrib-able to users’ behavior and lifestyle. The increase in buildings’ energy performance is strongly related to a smart management of the demand and self-consumption, as well as a more effective and active involvement of the occupants: it is, therefore, pivotal to come up with user-friendly tools to measure and monitor the performance of the buildings and users’ habits. Tools to encourage the choices toward the environment’s comfort, rather than automation technologies, allowing the occupants and informa-tion systems to move in the direction of ecological transition. The aim is to create an aware “energy citizenship” for people living in efficient buildings. The proposal is a system that uses IoT technology and provides a global evaluation of the state of the house, from which can be extracted suggestions for better and virtuous behavior. The overall ecological footprint is measured based on five “cycles”: energy; environment; water; waste production; food. Collected data create an urban database that, along with big data, constitutes a set of boundary conditions that are crossed with single units’ data. The measures related to single units can be applied to a wider network in order to create a smart city, involving dwellers in a serious game on their homes’ performance. The proposal is part of the research on post-evaluation occupancy, in the belief that even the best model-houses perform worse in use, rather than the predictions expected on paper