A Microforecasting Module for Energy Management in Residential and Tertiary Buildings †

Abstract

The paper describes the methodology used for developing an electric load microforecasting module to be integrated in the Energy Management System (EMS) architecture designed and tested within the “Energy Router” (ER) project. This Italian R&D project is aimed at providing non-industrial active customers and prosumers with a monitoring and control device that would enable demand response through optimization of their own distributed energy resources (DERs). The optimal control of resources is organized with a hierarchical control structure and performed in two stages. A cloud-based computation platform provides global control functions based on model predictive control whereas a closed-loop local device manages actual monitoring and control of field components. In this architecture, load forecasts on a small scale (a single residential or tertiary building) are needed as inputs of the predictive control problem. The microforecasting module aimed at providing such inputs was designed to be flexible, adaptive, and able to treat data with low time resolution. The module includes alternative forecasting techniques, such as autoregressive integrated moving average (ARIMA), neural networks, and exponential smoothing, allowing the application of the right forecasting strategy each time. The presented test results are based on a dataset acquired during a monitoring campaign in two pilot systems, installed during the ER Project in public buildings.

Demand and Storage Management in a Prosumer Nanogrid Based on Energy Forecasting

“…Indeed, optimal economical dispatch plays an important role in order to minimize the microgrid operating costs and reduce the energy costs compared with different strategies [21,23]. Hierarchical control structure and alternative forecasting techniques are implemented using autoregressive integrated moving average (ARIMA), exponential smoothing, and neural networks [24]. Some other optimal EMS incorporate the stochastic approach for microgrids using optimization algorithms of mixed-integrated linear programming (MILP), and GAMS (general algebraic modeling system) implementation using power optimization at minimum operational costs.…”

“…Along with this, weather forecasting strategies are needed in order to update the energy models to the local conditions and improve energy dispatch, taking advantage of all the energy resources. The introduction of weather forecasting models in predictive control [21,24] allows forecasting studies in a smaller scale (e.g., single residential buildings and tertiary buildings).…”

Primal-Dual Learning Based Risk-Averse Optimal Integrated Allocation of Hybrid Energy Generation Plants under Uncertainty

“…Nowadays, many new technologies involving environmentally friendly or clean energy generation infrastructures have emerged to meet industrial or domestic demands (e.g., nuclear energy, solar photovoltaic energy, hydroelectric energy and wind turbine energy, etc.). However, few of them have been able to play their full role, especially in reducing greenhouse emissions [2][3][4]. Moreover, certain technology might be well-meaning to human beings, but adverse to our living environments (e.g., nuclear energy).…”

Observer-Based Sliding Mode FTC for Multi-Area Interconnected Power Systems against Hybrid Energy Storage Faults

“…Several types of advanced energy storage technologies can be considered for the energy storage of a multi-area interconnected power system, e.g., lithium battery [15], electrochemical energy storage [16], superconducting magnetic energy storage [11][12][13], super capacitor energy storage [12,17], pumped-energy storage [18], flywheel energy storage [19]. Developing the electric load microforecasting modules in the energy management system provides a chance to optimally control the energy distribution in the power system [20]. However, any electronic component has a limited lifetime.…”