Energy Consumption Forecasting in Hong Kong Using ARIMA and Artificial Neural Networks Models

2014 ◽  
Vol 672-674 ◽  
pp. 2085-2097 ◽  
Author(s):  
Sue Ling Lai ◽  
Ming Liu ◽  
Kuo Cheng Kuo ◽  
Ray Chang
Keyword(s):  
Hong Kong ◽  
Energy Consumption ◽  
Energy Demand ◽  
Moving Average ◽  
Critical Role ◽  
Forecast Accuracy ◽  
Demand Forecast ◽  
Forecast Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

There have been considerable efforts contributed to the development of effective energy demand forecast models due to its critical role for economic development and environmental protection. This study focused on the adoption of artificial neural network (ANN) and autoregressive integrated moving average (ARIMA) models for energy consumption forecasting in Hong Kong over the period of 1975-2010. Four predictors were considered, including population, GDP, exports, and total visitor arrivals. The results show most ANN models demonstrate acceptable forecast accuracy when single predictor is considered. The best single input model is the case with GDP as predictor. Population and exports are the next proper single inputs. The model with total visitor arrivals as sole predictor does not perform satisfactorily. This indicates that tourism development demonstrates a different pattern from that of energy consumption. In addition, the forecast accuracy of ANN does not improve considerably as the number of predictors increase. Findings imply that with the ANN approach, choosing appropriate predictors is more important than increasing the number of predictors. On the other hand, ARIMA generates forecasts as accurate as some good cases by ANN. Results suggest that ARIMA is not only a parsimonious but effective approach for energy consumption forecasting in Hong Kong.

scholarly journals Industrial Facility Electricity Consumption Forecast Using Artificial Neural Networks and Incremental Learning

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4774
Author(s):  
Daniel Ramos ◽  
Pedro Faria ◽  
Zita Vale ◽  
João Mourinho ◽  
Regina Correia
Keyword(s):  
Energy Consumption ◽  
Energy Management ◽  
Incremental Learning ◽  
Forecast Accuracy ◽  
Electricity Consumption ◽  
Relevant Information ◽  
Industrial Facility ◽  
Artificial Neural ◽  
Reduction Of Energy Consumption ◽  
Artificial Neural Network Ann

Society’s concerns with electricity consumption have motivated researchers to improve on the way that energy consumption management is done. The reduction of energy consumption and the optimization of energy management are, therefore, two major aspects to be considered. Additionally, load forecast provides relevant information with the support of historical data allowing an enhanced energy management, allowing energy costs reduction. In this paper, the proposed consumption forecast methodology uses an Artificial Neural Network (ANN) and incremental learning to increase the forecast accuracy. The ANN is retrained daily, providing an updated forecasting model. The case study uses 16 months of data, split in 5-min periods, from a real industrial facility. The advantages of using the proposed method are illustrated with the numerical results.

scholarly journals Optimization of Performance Parameter Design and Energy Use Prediction for Nearly Zero Energy Buildings

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3252 ◽  
Author(s):  
Xiaolong Xu ◽  
Guohui Feng ◽  
Dandan Chi ◽  
Ming Liu ◽  
Baoyue Dou
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Energy Demand ◽  
Maximum Relative Error ◽  
Performance Parameters ◽  
Zero Energy ◽  
Saving Ratio ◽  
Zero Energy Building ◽  
Artificial Neural ◽  
Zero Energy Buildings

Optimizing key parameters with energy consumption as the control target can minimize the heating and cooling needs of buildings. In this paper we focus on the optimization of performance parameters design and the prediction of energy consumption for nearly Zero Energy Buildings (nZEB). The optimal combination of various performance parameters and the Energy Saving Ratio (ESR)are studied by using a large volume of simulation data. Artificial neural networks (ANNs) are applied for the prediction of annual electrical energy consumption in a nearly Zero Energy Building designs located in Shenyang (China). The data of the energy demand for our test is obtained by using building simulation techniques. The results demonstrate that the heating energy demand for our test nearly Zero Energy Building is 17.42 KW·h/(m2·a). The Energy Saving Ratio of window-to-wall ratios optimization is the most obvious, followed by thermal performance parameters of the window, and finally the insulation thickness. The maximum relative error of building energy consumption prediction is 6.46% when using the artificial neural network model to predict energy consumption. The establishment of this prediction method enables architects to easily and accurately obtain the energy consumption of buildings during the design phase.

scholarly journals STUDI KOMPARASI PREDIKSI CURAH HUJAN METODE FAST FOURIER TRANSFORMATION (FFT), AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) DAN ARTIFICIAL NEURAL NETWORK (ANN)

2015 ◽  
Vol 35 (02) ◽  
pp. 241
Author(s):  
Dyah Susilokarti ◽  
Sigit Supadmo Arif ◽  
Sahid Susanto ◽  
Lilik Sutiarso
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Time Series Data ◽  
Moving Average ◽  
Series Data ◽  
Fast Fourier Transformation ◽  
Autoregressive Integrated Moving Average ◽  
Precipitation Prediction ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Optimum climate condition and water availability are essential to support strategic venue and time for plants to grow and produce.  Precipitation prediction is needed to determine how much precipitation will provide water for plants on each stage of growth. Nowadays, the high variability of precipitation calls for a prediction model that will accurately foreseethe precipitation condition in the future. The prediction conducted is based on time-series data analysis. The research aims to comparethe effectiveness of three precipitation prediction methods, which are Fast Forier Transformation (FFT), Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN).  Their respective performances are determined by their Mean Square Error (MSE) values.  Methods with highest correlation values and lowest MSE shows the best performance. The MSE result for FFT is 14,92; ARIMA is 17,49; and  ANN is 0,07. This research concluded that Artificial Neural Network (ANN) method showed best performance compare to the other two because it had produced a prediction with the lowest MSE value.Keywords: Precipitation prediction, Fast Forier Transformation (FFT), Autoregressive Integrated Moving Average ABSTRAKKondisi iklim dan ketersediaan air yang optimal bagi pertumbuhan dan perkembangan tanaman sangat diperlukan dalam upaya mendukung strategi budidaya tanaman sesuai ruang dan waktu. Prediksi curah hujan sangat diperlukan untuk untuk mengetahui sejauh mana curah hujan dapat memenuhi kebutuhan air pada setiap tahap pertumbuhantanaman. Variabilitas curah hujan yang tinggi saat ini, membutuhkan pemodelan yang dapat memprediksi secara akurat bagaimana kondisi curah hujan dimasa yang akan datang. Prediksi yang dilakukan adalah prediksi berdasarkan urutan waktu ().  Tujuan dari penelitian ini adalah untuk membandingkan akurasi prediksi curah hujan antara metode  (FFT),  (ARIMA) dan (ANN). Kinerja ketiga metode yang digunakan dilihat dari nilai  (MSE). Metode dengan nilai korelasi tertinggi dan nilai MSE terkecil menunjukkan kinerja terbaik. Hasil penelitan untuk FFT diperoleh nilai MSE = 14,92, ARIMA = 17,49 sedangkan ANN = 0,07. Ini menunjukkan bahwa metode   (ANN) menunjukkan kinerja yang paling baik diantara dua metode lainnya karena menghasilkan prediksi yangmempunyai nilai MSE terkecil.Kata kunci: Prediksi curah hujan,FFT, ARIMA dan ANN 

scholarly journals Improving Hotel Room Demand Forecasts for Vienna across Hotel Classes and Forecast Horizons: Single Models and Combination Techniques Based on Encompassing Tests

Forecasting ◽  
2021 ◽  
Vol 3 (4) ◽  
pp. 884-919
Author(s):  
Ulrich Gunter
Keyword(s):  
Moving Average ◽  
Forecast Accuracy ◽  
Forecast Combination ◽  
Autoregressive Integrated Moving Average ◽  
Hotel Room ◽  
Daily Data ◽  
Forecast Models ◽  
Rolling Windows ◽  
Combined Forecasts ◽  
Box Cox Transformation

The present study employs daily data made available by the STR SHARE Center covering the period from 1 January 2010 to 31 January 2020 for six Viennese hotel classes and their total. The forecast variable of interest is hotel room demand. As forecast models, (1) Seasonal Naïve, (2) Error Trend Seasonal (ETS), (3) Seasonal Autoregressive Integrated Moving Average (SARIMA), (4) Trigonometric Seasonality, Box–Cox Transformation, ARMA Errors, Trend and Seasonal Components (TBATS), (5) Seasonal Neural Network Autoregression (Seasonal NNAR), and (6) Seasonal NNAR with an external regressor (seasonal naïve forecast of the inflation-adjusted ADR) are employed. Forecast evaluation is carried out for forecast horizons h = 1, 7, 30, and 90 days ahead based on rolling windows. After conducting forecast encompassing tests, (a) mean, (b) median, (c) regression-based weights, (d) Bates–Granger weights, and (e) Bates–Granger ranks are used as forecast combination techniques. In the relative majority of cases (i.e., in 13 of 28), combined forecasts based on Bates–Granger weights and on Bates–Granger ranks provide the highest level of forecast accuracy in terms of typical measures. Finally, the employed methodology represents a fully replicable toolkit for practitioners in terms of both forecast models and forecast combination techniques.

scholarly journals Energy Consumption Prediction in Vietnam with an Artificial Neural Network-Based Urban Growth Model

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4282
Author(s):  
Hye-Yeong Lee ◽  
Kee Moon Jang ◽  
Youngchul Kim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Growth Factors ◽  
Energy Consumption ◽  
Growth Model ◽  
Urban Growth ◽  
Energy Demand ◽  
Development Planning ◽  
Urban Growth Model ◽  
Artificial Neural

In developing countries, energy planning is important in the development planning due to high rates of economic growth and energy demand. However, existing approaches of energy prediction, using gross domestic product, hardly demonstrate how much energy specific regions or cities may need in the future. Thus, this study seeks to predict the amount of energy demand by considering urban growth as a crucial factor for investigating where and how much energy is needed. An artificial neural network is used to forecast energy patterns in Vietnam, which is a quickly developing country and seeks to have an adequate energy supply. Urban growth factors, population, and night-time light intensity are collected as an indicator of energy use. The proposed urban-growth model is trained with data of the years 1995, 2000, 2005, and 2010, and predicts the light distribution in 2015. We validated the model by comparing the predicted result with actual light data to display the spatial characteristics of energy-consumption patterns in Vietnam. In particular, the model with urban growth factors estimated energy consumption more closely to the actual consumption. This spatial prediction in Vietnam is expected to help plan geo-locational energy demands.

Predictability of HK-REITs returns using artificial neural network

2019 ◽  
Vol 38 (4) ◽  
pp. 291-307
Author(s):  
Wei Kang Loo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hong Kong ◽  
Capital Asset Pricing ◽  
Content Type ◽  
Forecasting Performance ◽  
Annual Time Series ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Annual Time

Purpose The purpose of this paper is to determine if artificial neural network (ANN) works better than linear regression in predicting Hong Kong real estate investment trusts’ (REITs) excess return. Design/methodology/approach Both ANN and the regression were applied in this study to forecast the Hong Kong REITs’ (HK-REITs) return using the capital asset pricing model and Fama and French’s three-factor models. Each result was further split into annual time series as a measure to investigate the consistency of the performance across time. Findings ANN had produced a better forecasting results than the regression based on their trading performance. However, the forecasting performance varied across individual REITs and time periods. Practical implications ANN should be considered for use when one were to attempt forecasting the HK-REITs excess returns. However, the trading performance should be always compared with buy and hold strategy prior to make any investment decisions. Originality/value This paper tested the predicting power of ANN on the HK-REITs and the consistency of its predicting power.

Predictive Analytics of Energy Usage by IoT-Based Smart Home Appliances for Green Urban Development

2022 ◽  
Vol 22 (2) ◽  
pp. 1-26
Author(s):  
Mohammad Shorfuzzaman ◽  
M. Shamim Hossain
Keyword(s):  
Energy Consumption ◽  
Urban Development ◽  
Predictive Models ◽  
Energy Demand ◽  
Smart Home ◽  
Predictive Analytics ◽  
Moving Average ◽  
Energy Usage ◽  
Total Energy Consumption ◽  
Home Appliances

Green IoT primarily focuses on increasing IoT sustainability by reducing the large amount of energy required by IoT devices. Whether increasing the efficiency of these devices or conserving energy, predictive analytics is the cornerstone for creating value and insight from large IoT data. This work aims at providing predictive models driven by data collected from various sensors to model the energy usage of appliances in an IoT-based smart home environment. Specifically, we address the prediction problem from two perspectives. Firstly, an overall energy consumption model is developed using both linear and non-linear regression techniques to identify the most relevant features in predicting the energy consumption of appliances. The performances of the proposed models are assessed using a publicly available dataset comprising historical measurements from various humidity and temperature sensors, along with total energy consumption data from appliances in an IoT-based smart home setup. The prediction results comparison show that LSTM regression outperforms other linear and ensemble regression models by showing high variability ( R 2 ) with the training (96.2%) and test (96.1%) data for selected features. Secondly, we develop a multi-step time-series model using the auto regressive integrated moving average (ARIMA) technique to effectively forecast future energy consumption based on past energy usage history. Overall, the proposed predictive models will enable consumers to minimize the energy usage of home appliances and the energy providers to better plan and forecast future energy demand to facilitate green urban development.

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