The comparison of medium-term energy demand forecasting methods for the need of microgrid management

Hossa, Tymoteusz; Filipowska, Agata; Fabisz, Karol

doi:10.1109/smartgridcomm.2014.7007711

Cited by 5 publications

(2 citation statements)

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“…Existing studies have shown that consumption prediction accuracy is high when consumption values of individual customers are aggregated together [6], [10], [11]. This is attributed to the law of large numbers such that larger the number of customers in an aggregated group, the lower the prediction error for the group [12]. Predictions for aggregated groups make it impossible to discover curtailment potential of individual customers, which is necessary for wider adoption of D 2 R. Models that work well for large commercial customers with smaller consumption variability over time, could be less efficient for small residential customers, whose consumption pattern fluctuates significantly.…”

Section: Introductionmentioning

confidence: 99%

Prediction models for dynamic demand response: Requirements, challenges, and insights

Aman

Frîncu

Chelmis

et al. 2015

2015 IEEE International Conference on Smart Grid Communications (SmartGridComm)

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As Smart Grids move closer to dynamic curtailment programs, Demand Response (DR) events will become necessary not only on fixed time intervals and weekdays predetermined by static policies, but also during changing decision periods and weekends to react to real-time demand signals. Unique challenges arise in this context vis-a-vis demand prediction and curtailment estimation and the transformation of such tasks into an automated, efficient dynamic demand response (D 2 R) process. While existing work has concentrated on increasing the accuracy of prediction models for DR, there is a lack of studies for prediction models for D 2 R, which we address in this paper. Our first contribution is the formal definition of D 2 R, and the description of its challenges and requirements. Our second contribution is a feasibility analysis of very-short-term prediction of electricity consumption for D 2 R over a diverse, large-scale dataset that includes both small residential customers and large buildings. Our third, and major contribution is a set of insights into the predictability of electricity consumption in the context of D 2 R. Specifically, we focus on prediction models that can operate at a very small data granularity (here 15-min intervals), for both weekdays and weekends -all conditions that characterize scenarios for D 2 R. We find that short-term time series and simple averaging models used by Independent Service Operators and utilities achieve superior prediction accuracy. We also observe that workdays are more predictable than weekends and holiday. Also, smaller customers have large variation in consumption and are less predictable than larger buildings. Key implications of our findings are that better models are required for small customers and for non-workdays, both of which are critical for D 2 R. Also, prediction models require just few days' worth of data indicating that small amounts of historical training data can be used to make reliable predictions, simplifying the complexity of big data challenge associated with D 2 R.

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Section: Introductionmentioning

confidence: 99%

Prediction models for dynamic demand response: Requirements, challenges, and insights

Aman

Frîncu

Chelmis

et al. 2015

2015 IEEE International Conference on Smart Grid Communications (SmartGridComm)

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“…Although weather forecast parameters have a significant influence on the predictive models, especially outdoor temperature [19][20][21], they have an ancillary treatment in the literature [22] and we can find only a few articles where the effect of weather forecast uncertainty is analyzed and quantified: Henze et al [23] analyze the effect of the uncertainty using different weather prediction models on the performance of a predictive control concept. The conclusion is that it takes elementary short-term prediction models to realize almost all of the theoretical potential of the predictive optimal control technique.…”

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confidence: 99%

Methodology for the Quantification of the Impact of Weather Forecasts in Predictive Simulation Models

Segarra

Ruiz

et al. 2019

Energies

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The use of Building Energy Models (BEM) has become widespread to reduce building energy consumption. Projection of the model in the future to know how different consumption strategies can be evaluated is one of the main applications of BEM. Many energy management optimization strategies can be used and, among others, model predictive control (MPC) has become very popular nowadays. When using models for predicting the future, we have to assume certain errors that come from uncertainty parameters. One of these uncertainties is the weather forecast needed to predict the building behavior in the near future. This paper proposes a methodology for quantifying the impact of the error generated by the weather forecast in the building’s indoor climate conditions and energy demand. The objective is to estimate the error introduced by the weather forecast in the load forecasting to have more precise predicted data. The methodology employed site-specific, near-future forecast weather data obtained through online open access Application Programming Interfaces (APIs). The weather forecast providers supply forecasts up to 10 days ahead of key weather parameters such as outdoor temperature, relative humidity, wind speed and wind direction. This approach uses calibrated EnergyPlus models to foresee the errors in the indoor thermal behavior and energy demand caused by the increasing day-ahead weather forecasts. A case study investigated the impact of using up to 7-day weather forecasts on mean indoor temperature and energy demand predictions in a building located in Pamplona, Spain. The main novel concepts in this paper are: first, the characterization of the weather forecast error for a specific weather data provider and location and its effect in the building’s load prediction. The error is calculated based on recorded hourly data so the results are provided on an hourly basis, avoiding the cancel out effect when a wider period of time is analyzed. The second is the classification and analysis of the data hour-by-hour to provide an estimate error for each hour of the day generating a map of hourly errors. This application becomes necessary when the building takes part in the day-ahead programs such as demand response or flexibility strategies, where the predicted hourly load must be provided to the grid in advance. The methodology developed in this paper can be extrapolated to any weather forecast provider, location or building.

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Weather forecasts for microgrid energy management: Review, discussion and recommendations

et al. 2018

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The comparison of medium-term energy demand forecasting methods for the need of microgrid management

Cited by 5 publications

References 25 publications

Prediction models for dynamic demand response: Requirements, challenges, and insights

Prediction models for dynamic demand response: Requirements, challenges, and insights

Methodology for the Quantification of the Impact of Weather Forecasts in Predictive Simulation Models

Weather forecasts for microgrid energy management: Review, discussion and recommendations

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