Space‐Time Model versus VAR Model: Forecasting Electricity demand in Japan

Ohtsuka, Yoshihiro; Kakamu, Kazuhiko

doi:10.1002/for.1255

Cited by 17 publications

(8 citation statements)

References 28 publications

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“…One reason for this was that we extended our model structure based on the time series analysis in Ohtsuka et al . () and Ohtsuka and Kakamu (). However, we also need to examine the dynamic panel model approach.…”

Section: Discussionmentioning

confidence: 99%

Regional Growth and Business Cycles in Japan

Ohtsuka

Kakamu²

2017

Rev Urban & Reg Develop

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This study examines the consistency and gaps in national and regional business cycles in Japan from a Bayesian point of view. The Tokyo monopolar system started in the mid-1970s, and recent descriptive statistics, such as migration and per capita income, show that the system continues, despite severe crises such as the burst of the 1990s economic bubble and the Lehman Brothers bankruptcy. We explore the relationship between national and regional business cycles in the system using a spatio-temporal Markov-switching model with the Markov chain Monte Carlo method. Our empirical results show that overall, the regional business cycle in the Kanto region, including Tokyo, is identical to the national business cycle. Moreover, we find that switches in the degree of spatial dependency occur around the turning points of business cycles, and that the degree of spatial dependency tends to be higher during a recession.

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Section: Discussionmentioning

confidence: 99%

Regional Growth and Business Cycles in Japan

Ohtsuka

Kakamu²

2017

Rev Urban & Reg Develop

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“…Angulo and Trívez (2010) find a substantial equivalence between a fixed effects panel spatial lag model and a series of non-spatial ARIMA models. On the other hand, Ohtsuka and Kakamu (2013) find that a VAR model outperforms a spatial autoregressive ARMA (SAR-ARMA) model.…”

Section: Methodology: Spatial Vector-autoregressive Models and Spatiamentioning

confidence: 99%

Spatial Panel Data Forecasting over Different Horizons, Cross-Sectional and Temporal Dimensions

Mayor

Patuelli

2013

SSRN Journal

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Empirical assessments of the forecasting power of spatial panel data econometric models are still scarcely available. Moreover, several methodological contributions rely on simulated data to showcase the potential of proposed methods. While simulations may be useful to evaluate the properties of a single estimator, the empirical set-ups of simulation studies are often based on strong assumptions regarding the shape and regularity of the statistical distribution of the variables involved. It is then valuable to have, next to simulation studies, empirical assessments of competing econometric models based on real data. In this paper, we evaluate competing spatial (dynamic) panel methods, selecting a number of data sets characterized by a range of different crosssectional and temporal dimensions, as well as different levels of spatial autocorrelation. We carry out our empirical exercise on regional unemployment data for France, Spain and Switzerland. Additionally, we test different forecasting horizons, in order to investigate the speed of deterioration of forecasting quality. We compare two classes of methods: spatial vector autoregressive (SVAR) models and dynamic panel models making use of eigenvector spatial filtering (SF). We find that, as it could be expected, the unbalance between the temporal and cross-sectional dimension (T >> n) does play in favour of the SVAR model. On the other hand, the advantage of the SVAR model over the SF model appears to diminish as the forecasting horizon widens, eventually leading the SF model to being preferred for more distant forecasts.

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“…Let us mention some references where SSM have been used to forecast load demand. Classical vector autoregressive processes are used in [6] under the form of SSM to compare the predictive performance with respect to seasonal autoregressive processes. The main point in [7] is to combine several machine learning techniques with wavelet transforms of the electrical signal.…”

Section: Introductionmentioning

confidence: 99%

“…Variants considered for the model(6) showing different structures of matrices H i and Q i and number of unknown parameters as function of p.…”

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confidence: 99%

Short-Term Electricity Demand Forecasting Using a Functional State Space Model

2018

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In the past several years, the liberalization of the electricity supply, the increase in variability of electric appliances and their use, and the need to respond to the electricity demand in real time has made electricity demand forecasting a challenge. To this challenge, many solutions are being proposed. The electricity demand involves many sources such as economic activities, household need and weather sources. All of these sources make electricity demand forecasting difficult. To forecast the electricity demand, some proposed parametric methods that integrate main variables that are sources of electricity demand. Others proposed a non parametric method such as pattern recognition methods. In this paper, we propose to take only the past electricity consumption information embedded in a functional vector autoregressive state space model to forecast the future electricity demand. The model we proposed aims to be applied at some aggregation level between regional and nationwide grids. To estimate the parameters of this model, we use likelihood maximization, spline smoothing, functional principal components analysis and Kalman filtering. Through numerical experiments on real datasets, both from supplier Enercoop and from the Transmission System Operator of the French nationwide grid, we show the appropriateness of the approach.

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Space‐Time Model versus VAR Model: Forecasting Electricity demand in Japan

Cited by 17 publications

References 28 publications

Regional Growth and Business Cycles in Japan

Regional Growth and Business Cycles in Japan

Spatial Panel Data Forecasting over Different Horizons, Cross-Sectional and Temporal Dimensions

Short-Term Electricity Demand Forecasting Using a Functional State Space Model

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