Multi-Step Energy Demand and Generation Forecasting with Confidence Used for Specification-Free Aggregate Demand Optimization

Abstract

Energy demand and generation are common variables that need to be forecast in recent years, due to the necessity for energy self-consumption via storage and Demand Side Management. This work studies multi-step time series forecasting models for energy with confidence intervals for each time point, accompanied by a demand optimization algorithm, for energy management in partly or completely isolated islands. Particularly, the forecasting is performed via numerous traditional and contemporary machine learning regression models, which receive as input past energy data and weather forecasts. During pre-processing, the historical data are grouped into sets of months and days of week based on clustering models, and a separate regression model is automatically selected for each of them, as well as for each forecasting horizon. Furthermore, the multi-criteria optimization algorithm is implemented for demand scheduling with load shifting, assuming that, at each time point, demand is within its confidence interval resulting from the forecasting algorithm. Both clustering and multiple model training proved to be beneficial to forecasting compared to traditional training. The Normalized Root Mean Square Error of the forecasting models ranged approximately from 0.17 to 0.71, depending on the forecasting difficulty. It also appeared that the optimization algorithm can simultaneously increase renewable penetration and achieve load peak shaving, while also saving consumption cost in one of the tested islands. The global improvement estimation of the optimization algorithm ranged approximately from 5% to 38%, depending on the flexibility of the demand patterns.

DSF Core: Integrated Decision Support for Optimal Scheduling of Lifetime Extension Strategies for Industrial Equipment

“…The mathematical formulation of the optimization problem to be solved by the DSF Core algorithm in RECLAIM was inspired mainly by the MCDA approach of [ 21 ], which was also taken into account by the authors in their previous work on demand-side management [ 77 ] and is suitable for integrating economic and environmental KPIs [ 68 ]. However, this method in those papers is not combined with life cycle evaluation of KPIs, which enables decision support considering the strategy application policy impact on their values both during the application of strategies and in the long run.…”

Electricity Demand Forecasting with Use of Artificial Intelligence: The Case of Gokceada Island

“…electricity imports and export, population, installed capacity, and gross electricity generation Ramsami and King [11] electricity demand adaptive network-based fuzzy inference system, ANN, RNN historical electricity data Bendaoud et al [12] electrical energy demand CNN load profile Sen et al [13] electricity consumption ANN-SVM population, GDP, inflation rate, and unemployment rate Tun et al [14] energy demand RNN past energy usage data Kolokas et al [15] energy demand and generation multi-step time series forecasting past energy data and weather forecasts Al-Musaylh et al [16] electricity demand online sequential extreme learning machine (OS-ELM) climate variables Moustris et al [17] load demand ANN meteorological data, cooling power index (CP)…”

A Data-Driven Forecasting Strategy to Predict Continuous Hourly Energy Demand in Smart Buildings

“…The studies mentioned above, as well as others for building energy consumption forecasting, use a one-step forecasting strategy, which consists of predicting the next step. Within the same category, a strategy that is not so commonly used but has shown promising results in different studies [16][17][18] is the multi-step forecasting strategy that allows multiple future steps to be predicted. Considering the aforementioned, this paper's objective is to present an energy consumption forecasting methodology that allows estimating energy consumption of the next 24 h at any hour of the day using a direct multi-step ahead forecasting strategy.…”