Probabilistic load forecasting for buildings considering weather forecasting uncertainty and uncertain peak load

Xu, Lei; Wang, Shengwei; Tang, Rui

doi:10.1016/j.apenergy.2019.01.022

Cited by 118 publications

(37 citation statements)

References 26 publications

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“…Many forecasting models have been used in various studies [32]- [34] for load prediction. In this study, we have used an ELM forecasting model for time-series load prediction and further, segregation of LECs and HECs on the basis of a reliable load prediction.…”

Section: Forecasting Modelmentioning

confidence: 99%

A Fair Pricing Mechanism in Smart Grids for Low Energy Consumption Users

et al. 2021

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Based on energy demand, consumers can be broadly categorized into low energy consumers (LECs) and high energy consumers (HECs). HECs use heavy load appliances, e.g., electric heaters and air conditioners, and LECs do not use heavy load appliances. Thus, HECs demand more energy compared to LECs. The usage of high energy consumption appliances by HECs leads to peak formation in various time intervals. Different pricing schemes, i.e., time of use (ToU), real time pricing (RTP), inclined block rate (IBR), and critical peak pricing (CPP), have been proposed previously. In ToU, an energy tariff is divided into three blocks, i.e., on-peak (high rates), off-peak (low rates), and mid-peak (between on-peak and offpeak rates) hours, and these rates are applied to all electricity users without distinction. The high energy demand by HECs causes the high peak formation; thus, higher rates should be applied to only HECs rather than all consumers, which is not the case in existing billing mechanisms. LECs are also charged higher rates in on-peak intervals and this billing mechanisms are unjustified. Thus, in this paper, a fair pricing scheme (FPS) based on power demand forecasting is developed to reduce extra bills of LECs. First, we developed a machine learning-based electricity load forecasting method, i.e., an extreme learning machine (ELM), in order to differentiate LECs and HECs. With the proposed FPS, electricity cost calculations for LECs and HECs are based on the actual energy consumption; thus, LECs do not subsidize HECs. Simulations were conducted for performance evaluation of our proposed FPS mechanism, and the results demonstrate LECs can reduce electricity cost up to 11.0075%, and HECs are charged relatively higher than previous pricing schemes as a penalty for their contribution to the on-peak formation. INDEX TERMS Smart grid; low energy consumers; pricing tariff; load forecasting; extreme learning machines; time of use; fair pricing scheme;

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Section: Forecasting Modelmentioning

confidence: 99%

A Fair Pricing Mechanism in Smart Grids for Low Energy Consumption Users

et al. 2021

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“…In this study, the aggregated load of about 60 consumers from an Italian LV network was used to demonstrate the performance of the proposed approach. The work in [26] presented a probabilistic load forecasting model in which weather forecasting uncertainty was used. The artificial neural network (ANN) and the probabilistic temperature forecasts were used to build this probabilistic normal load forecasting model.…”

Section: Load Forecasting In Distribution Systemsmentioning

confidence: 99%

Single Residential Load Forecasting Using Deep Learning and Image Encoding Techniques

Estebsari

Rajabi

2020

Electronics

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The integration of more renewable energy resources into distribution networks makes the operation of these systems more challenging compared to the traditional passive networks. This is mainly due to the intermittent behavior of most renewable resources such as solar and wind generation. There are many different solutions being developed to make systems flexible such as energy storage or demand response. In the context of demand response, a key factor is to estimate the amount of load over time properly to better manage the demand side. There are many different forecasting methods, but the most accurate solutions are mainly found for the prediction of aggregated loads at the substation or building levels. However, more effective demand response from the residential side requires prediction of energy consumption at every single household level. The accuracy of forecasting loads at this level is often lower with the existing methods as the volatility of single residential loads is very high. In this paper, we present a hybrid method based on time series image encoding techniques and a convolutional neural network. The results of the forecasting of a real residential customer using different encoding techniques are compared with some other existing forecasting methods including SVM, ANN, and CNN. Without CNN, the lowest mean absolute percentage of error (MAPE) for a 15 min forecast is above 20%, while with existing CNN, directly applied to time series, an MAPE of around 18% could be achieved. We find the best image encoding technique for time series, which could result in higher accuracy of forecasting using CNN, an MAPE of around 12%.

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“…If it deviates considerably away from 60 Hz, we may have a grid-scale failure. Hence, to assure reliability, it requires to continually adjust the availability of supply to match it to demand within a certain range and keep frequency close to 60 Hz [86], [87].…”

Section: B Load Forecasting Techniquesmentioning

confidence: 99%

A Qualitative Study on the United States Internet of Energy: A Step Towards Computational Sustainability

2020

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The burgeoning growth of Big Data not only matures and improves the data management efficiency and useful information extraction techniques, but also motivates the computational science researchers to come up with a new method or solution that can be repurposed for problems across the domain. Computational Sustainability joins this movement for a transferrable computational technique for sustainable development and a better future. Internet-of-energy (IoE)-leveraging IoT to smart grids associated with advanced analytics-is one of the prominent efforts in this regard. This paper presents a qualitative analysis on the elements of the energy and power management ecosystem in the United States. This qualitative study includes the Grid Overview of the United States; Weather and Climate and its impact on the entire energy generation and consumption dynamics; Peak Load Forecasting and its techniques and burgeoning challenges; Variable Renewable Energy, its reliability challenges and how we can take advantage of this variability; Commodity Prices and its criticality; Energy Disaggregation and its impact on consumptionawareness; and Generation Expansion and Decision Analysis. Besides, IoE integration, associated tradeoffs, challenges, research opportunities and transferable computational techniques are addressed in this communication. Furthermore, schematics and quantitative analysis are presented in support of this study.INDEX TERMS Computational sustainability, Internet of Things, smart grid, Internet of Energy, big data, transferable computational techniques.

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Probabilistic load forecasting for buildings considering weather forecasting uncertainty and uncertain peak load

Cited by 118 publications

References 26 publications

A Fair Pricing Mechanism in Smart Grids for Low Energy Consumption Users

A Fair Pricing Mechanism in Smart Grids for Low Energy Consumption Users

Single Residential Load Forecasting Using Deep Learning and Image Encoding Techniques

A Qualitative Study on the United States Internet of Energy: A Step Towards Computational Sustainability

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