Residential Power Load Forecasting

Day, Patrick; Fabian, Michael; Noble, Don; Ruwisch, George; Spencer, Ryan; Stevenson, J. T.; Thoppay, Rajesh

doi:10.1016/j.procs.2014.03.056

Cited by 10 publications

(10 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These sources generate data such as: hourly/daily load history at end user level, hourly weather history at weather stations, demographic and economy information, industry code mapping, outage logs, and energy system loss information [5] [15]. Additional attributes may include the effects of varying sunrise and sunset times which determine when domestic and street lighting are used.…”

Section: Related Workmentioning

confidence: 99%

“…For example, in New England area Independence Day may be modeled as typical Sunday, while Thanksgiving Day is most similar to Saturday [9]. A comprehensive short term load model should make a distinction between three classes of electricity customers: residential, commercial, and industrial customers [5] [8] [9]. Residential customers have the most weather responsive electricity consumption behavior.…”

Section: Related Workmentioning

confidence: 99%

“…The problem is that some input data which are source of irregularities are not used to build the model. Usually, in current models, these effects are interpreted as outliers or random peaks, which are removed to "improve the model" [5]. Therefore, we have to revisit the issue of outlier removal which are correct measurements, with useful information that we wrongly excluded [16].…”

Section: Short Term Load Forecasting Using Web-based Social Network Amentioning

confidence: 99%

“…Forecasting on a level of smaller geo-locations is obviously more difficult due to variability of human behavior. Because of a complexity and the amount of data, forecasting problem may be classified as big data problem requiring new approaches to computing [5]. The techniques from different fields, such as artificial intelligence, high performance computing, data mining, simulation and stochastic modeling, are employed for the development of forecasting subsystems on a smart grid.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Improved Short Term Energy Load Forecasting Using Web-Based Social Networks

Kantardzic¹,

Gavranovic²,

Gavranović³

et al. 2015

View full text Add to dashboard Cite

In this article, we are initiating the hypothesis that improvements in short term energy load forecasting may rely on inclusion of data from new information sources generated outside the power grid and weather related systems. Other relevant domains of data include scheduled activities on a grid, large events and conventions in the area, equipment duty cycle schedule, data from call centers, real-time traffic, Facebook, Twitter, and other social networks feeds, and variety of city or region websites. All these distributed data sources pose information collection, integration and analysis challenges. Our approach is concentrated on complex non-cyclic events detection where detected events have a human crowd magnitude that is influencing power requirements. The proposed methodology deals with computation, transformation, modeling, and patterns detection over large volumes of partially ordered, internet based streaming multimedia signals or text messages. We are claiming that traditional approaches can be complemented and enhanced by new streaming data inclusion and analyses, where complex event detection combined with Webbased technologies improves short term load forecasting. Some preliminary experimental results, using Gowalla social network dataset, confirmed our hypothesis as a proof-of-concept, and they paved the way for further improvements by giving new dimensions of short term load forecasting process in a smart grid.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Short Term Load Forecasting Using Web-based Social Network Amentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improved Short Term Energy Load Forecasting Using Web-Based Social Networks

Kantardzic¹,

Gavranovic²,

Gavranović³

et al. 2015

View full text Add to dashboard Cite

show abstract

“…Figure 1 is an example of the percentage differences between the actual load and forecast load consumption, used by different classes of consumers from different locations in Australia. The Negative (−ve) bars and points shown above the x-axis in Figure 1a-c mean the load forecasting values were low compared to the actual consumption after Sustainability 2017, 9,1972 2 of 27 calculating their differences. In addition, the positive (+ve) bars and points shown below the x-axis in Figure 1a-c depict the forecasting values were high in relation to the actual electricity consumption after their computation differences.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Multiple Forecasting of Electric Energy Loads for Sustainable Demand Planning in Smart Grids for Smart Homes

Alani

Osunmakinde

2017

Sustainability

View full text Add to dashboard Cite

Abstract:Energy consumption in the form of fuel or electricity is ubiquitous globally. Among energy types, electricity is crucial to human life in terms of cooking, warming and cooling of shelters, powering of electronic devices as well as commercial and industrial operations. Users of electronic devices sometimes consume fluctuating amounts of electricity generated from smart-grid infrastructure owned by the government or private investors. However, frequent imbalance is noticed between the demand and supply of electricity, hence effective planning is required to facilitate its distribution among consumers. Such effective planning is stimulated by the need to predict future consumption within a short period. Although several interesting classical techniques have been used for such predictions, they still require improvement for the purpose of reducing significant predictive errors when used for short-term load forecasting. This research develops a near-zero cooperative probabilistic scenario analysis and decision tree (PSA-DT) model to address the lacuna of enormous predictive error faced by the state-of-the-art models. The PSA-DT is based on a probabilistic technique in view of the uncertain nature of electricity consumption, complemented by a DT to reinforce the collaboration of the two techniques. Based on detailed experimental analytics on residential, commercial and industrial data loads, the PSA-DT model outperforms the state-of-the-art models in terms of accuracy to a near-zero error rate. This implies that its deployment for electricity demand planning will be of great benefit to various smart-grid operators and homes.

show abstract