Optimal Energy Consumption Forecast for Grid Responsive Buildings: A Sensitivity Analysis

Abstract

It is envisioned that building systems will become active participants in the smart grid operation by controlling their energy consumption to optimize complex criteria beyond ensuring local end-use comfort satisfaction. A forecast of the building energy consumption will be necessary to enable integration between building and grid operation. Such forecast will be affected by parametric and measurement uncertainty. In this paper we develop a methodology for quantifying the sensitivity of optimal hourly energy consumption forecasts to various sources model and measurement uncertainty. We demonstrate the approach for a building heating ventilation and air conditioning (HVAC) system use-case.

Modelling and forecasting of electrical consumption for demand response applications

“…The tool is calibrated using the values a in interval a ∈ [3,4], and b on interval b ∈ [4,5]. The calibration was performed by analyzing input data using clustering algorithms and the values were placed in (4).…”

“…[1] To some extend the technology society seems to approach its limits and technological progress is no longer selfsufficient in the context of energy efficiency. Proposed solutions include prosumer engagement, demand response, demand side management, building energy management systems and other optimizations [4].…”

Time Series Clustering of Electricity Demand for Industrial Areas on Smart Grid

“…They showed that the optimal model was the ANN model with external variables of weather and holiday effects over the time horizons. Kundu et al [31] worked on the uncertainty of parameters and measurements for hourly energy consumption forecasts. They analyzed the sensitivity of the optimization with commercial heating ventilation and air conditioning (HVAC) system data.…”