IoT based Online Load Forecasting

被引：9

作者：

Saber, Ahmed Yousuf ^{[1
]}

Khandelwal, Tanuj ^{[1
]}

机构：

[1] ETAP, Irvine, CA 92618 USA

来源：

2017 NINTH ANNUAL IEEE GREEN TECHNOLOGIES CONFERENCE (GREENTECH 2017) | 2017年

关键词：

Internet of Things; multi-objective optimization; neural networks; particle swarm optimization; short term load forecasting; NEURAL-NETWORK; PATTERN-RECOGNITION;

D O I：

10.1109/GreenTech.2017.34

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

Load forecasting is a data intensive statistical method. Internet of things (IoT) based online load forecasting (LF) collects those data from internet on demand and then performs fast statistical and optimization methods for forecasting efficiently. IoT based online LF not only depends on power systems properties, but also internet, machine-to-machine (M2M) connections, communications and computation facilities. Limitations for better utilization, reliability, stability and control of smart grid technologies make it different from traditional load forecasting. Power systems are typically large, complex and distributed. In this study, load data is collected from smart meters and stored as historical load data. However, weather data at a given geographical location including temperature, humidity, wind speed, wind direction, heat, sunlight, solar radiation, rainfall and so on with good accuracy are collected from internet on demand. Computations are done in two steps: first neural network (NN) training to map the dynamics of load and then an optimization on the NN weights to improve overall forecasting error. NN is an effective mathematical tool for mapping complex relationships. On the other hand, particle swarm optimization (PSO) is used because it is the most promising swarm based optimization tool. Results show the effectiveness of the proposed online short term load forecasting in IoT.

引用

页码：189 / 194

页数：6

共 25 条

[1] Ahmmed Suman, 2009, Proceedings of the 2009 12th International Conference on Computer and Information Technology (ICCIT 2009), P290, DOI 10.1109/ICCIT.2009.5407122
[2] Applying Wavelets to Short-Term Load Forecasting Using PSO-Based Neural Networks
Bashir, Z. A.
El-Hawary, M. E.
[J]. IEEE TRANSACTIONS ON POWER SYSTEMS, 2009, 24 (01) : 20 - 27
[3] Load forecasting using support vector machines: A study on EUNITE competition 2001
Chen, BJ
Chang, MW
Lin, CJ
[J]. IEEE TRANSACTIONS ON POWER SYSTEMS, 2004, 19 (04) : 1821 - 1830
[4] Chen H, 2001, 2001 IEEE POWER ENGINEERING SOCIETY WINTER MEETING, CONFERENCE PROCEEDINGS, VOLS 1-3, P411, DOI 10.1109/PESW.2001.916876
[5] SHORT-TERM LOAD FORECASTING USING AN ADAPTIVE NEURAL NETWORK
DILLON, TS
SESTITO, S
LEUNG, S
[J]. INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 1991, 13 (04) : 186 - 192
[6] Short-term load forecasting with local ANN predictors
Drezga, I
Rahman, S
[J]. IEEE TRANSACTIONS ON POWER SYSTEMS, 1999, 14 (03) : 844 - 850
[7] Short-Term Load Forecasting Using Fuzzy Inductive Reasoning and Evolutionary Algorithms
Hinojosa, V. H.
Hoese, A.
[J]. IEEE TRANSACTIONS ON POWER SYSTEMS, 2010, 25 (01) : 565 - 574
[8] Neural networks for short-term load forecasting: A review and evaluation
Hippert, HS
Pedreira, CE
Souza, RC
[J]. IEEE TRANSACTIONS ON POWER SYSTEMS, 2001, 16 (01) : 44 - 55
[9] Kennedy J, 1995, 1995 IEEE INTERNATIONAL CONFERENCE ON NEURAL NETWORKS PROCEEDINGS, VOLS 1-6, P1942, DOI 10.1109/icnn.1995.488968
[10] Bayesian neural network approach to short time load forecasting
Lauret, Philippe
Fock, Eric
Randrianarivony, Rija N.
Manicom-Ramasamy, Jean-Francois
[J]. ENERGY CONVERSION AND MANAGEMENT, 2008, 49 (05) : 1156 - 1166

← 1 2 3 →